Data Scientist - Take Home Test

Background

I am an aspiring YouTuber and my dream is for one of my future videos to be trending. In order to do so, I want to get some insights from the past trending videos on YouTube. My friend already helped me to extract the data regarding trending videos, but neither of us have the skill to analyze the data.

Objectives

Please provide me a summary regarding the common factors of the trending videos, some things that I could think of:

  1. specific topic/category that the video falls onto
  2. who uploaded these videos
  3. what are written on the title, tags, or description
  4. the engagement metrics (views, likes, dislikes, comments)
  5. or anything else that you can find

I understand that there might be different combinations that allow videos to be trending, so I appreciate if you could also provide me with video groups/segmentations with proper labelling (e.g. Group 1 - Superhero Trailers, Group 2 - Videos with animal tag) and relevant statistics.

Metadata

The following are some descriptions for the given columns in each dataset. If some columns are not mentioned, you may ignore those for further analytics.

youtube-videos.parquet

  • video_id: unique ID for a Youtube video, note that this value is NOT unique in this dataset (the same video can be listed more than once on multiple trending_date)
  • trending_date: when the specific video is trending (in YY.DD.MM format)
  • title: title of the video (what you see before clicking on a YT video)
  • channel_title: channel that published the video
  • category_id: unique ID for the video category (see column id in the other dataset)
  • publish_time: when the video was published (in YYYY-MM-DDThh:mm:ssZ format) tags: YouTube tags, separated by delimiter “|” views, likes, dislikes, comment_count: user engagement metrics towards the video
  • comments_disabled: whether comment is disabled for the video
  • ratings_disabled: whether rating system is disabled for the video
  • description: extra sentences written by the uploader to explain the video content

youtube-categories.json

  • id: unique ID for the category, refers to category_id on the other dataset
  • title: title of the given category
  • channel_id: unique identifier for the channel

Libraries

In [1]:
!pip install contractions
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In [2]:
!pip install nltk==3.7
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In [3]:
!pip install kmodes
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In [4]:
!pip install pyldavis
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In [5]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
from google.colab import drive

import nltk
import string
import contractions
from nltk.tokenize import word_tokenize
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords, wordnet
from nltk.stem import WordNetLemmatizer
from wordcloud import WordCloud
from collections import Counter
from nltk.probability import FreqDist
from nltk.util import ngrams
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation, NMF
from scipy import stats
from sklearn.preprocessing import LabelEncoder  
from sklearn.preprocessing import StandardScaler  
from kmodes.kmodes import KModes  
from kmodes.kprototypes import KPrototypes
import pyLDAvis.sklearn

sns.set_theme(style="darkgrid", palette='flare')
/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
  from collections import Iterable
/usr/local/lib/python3.7/dist-packages/past/builtins/misc.py:4: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
  from collections import Mapping

Data Preparation

In [6]:
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
In [7]:
df1 = pd.read_parquet('/content/gdrive/MyDrive/youtube-videos.parquet')
df1.head()
Out[7]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count thumbnail_link comments_disabled ratings_disabled video_error_or_removed description
0 2kyS6SvSYSE 17.14.11 WE WANT TO TALK ABOUT OUR MARRIAGE CaseyNeistat 22 2017-11-13T17:13:01.000Z SHANtell martin 748374 57527 2966 15954 https://i.ytimg.com/vi/2kyS6SvSYSE/default.jpg False False False SHANTELL'S CHANNEL - https://www.youtube.com/s...
1 1ZAPwfrtAFY 17.14.11 The Trump Presidency: Last Week Tonight with J... LastWeekTonight 24 2017-11-13T07:30:00.000Z last week tonight trump presidency|"last week ... 2418783 97185 6146 12703 https://i.ytimg.com/vi/1ZAPwfrtAFY/default.jpg False False False One year after the presidential election, John...
2 5qpjK5DgCt4 17.14.11 Racist Superman | Rudy Mancuso, King Bach & Le... Rudy Mancuso 23 2017-11-12T19:05:24.000Z racist superman|"rudy"|"mancuso"|"king"|"bach"... 3191434 146033 5339 8181 https://i.ytimg.com/vi/5qpjK5DgCt4/default.jpg False False False WATCH MY PREVIOUS VIDEO ▶ \n\nSUBSCRIBE ► http...
3 puqaWrEC7tY 17.14.11 Nickelback Lyrics: Real or Fake? Good Mythical Morning 24 2017-11-13T11:00:04.000Z rhett and link|"gmm"|"good mythical morning"|"... 343168 10172 666 2146 https://i.ytimg.com/vi/puqaWrEC7tY/default.jpg False False False Today we find out if Link is a Nickelback amat...
4 d380meD0W0M 17.14.11 I Dare You: GOING BALD!? nigahiga 24 2017-11-12T18:01:41.000Z ryan|"higa"|"higatv"|"nigahiga"|"i dare you"|"... 2095731 132235 1989 17518 https://i.ytimg.com/vi/d380meD0W0M/default.jpg False False False I know it's been a while since we did this sho...
In [8]:
df2 = pd.read_json("/content/gdrive/MyDrive/youtube-categories.json")
df2
Out[8]:
kind etag id snippet
0 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/Xy1mB4_yLrHy_BmKm... 1 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
1 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/UZ1oLIIz2dxIhO45Z... 2 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
2 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/nqRIq97-xe5XRZTxb... 10 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
3 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/HwXKamM1Q20q9BN-o... 15 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
4 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/9GQMSRjrZdHeb1OEM... 17 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
5 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/FJwVpGCVZ1yiJrqZb... 18 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
6 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/M-3iD9dwK7YJCafRf... 19 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
7 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/WmA0qYEfjWsAoyJFS... 20 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
8 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/EapFaGYG7K0StIXVf... 21 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
9 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/xId8RX7vRN8rqkbYZ... 22 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
10 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/G9LHzQmx44rX2S5ya... 23 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
11 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/UVB9oxX2Bvqa_w_y3... 24 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
12 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/QiLK0ZIrFoORdk_g2... 25 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
13 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/r6Ck6Z0_L0rG37VJQ... 26 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
14 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/EoYkczo9I3RCf96Rv... 27 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
15 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/w5HjcTD82G_XA3xBc... 28 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
16 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/SalkJoBWq_smSEqiA... 29 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
17 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/lL7uWDr_071CHxifj... 30 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
18 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/WnuVfjO-PyFLO7NTR... 31 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
19 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/ctpH2hGA_UZ3volJT... 32 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
20 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/L0kR3-g1BAo5UD1PL... 33 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
21 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/pUZOAC_s9sfiwar63... 34 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
22 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/Xb5JLhtyNRN3AQq02... 35 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
23 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/u8WXzF4HIhtEi805_... 36 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
24 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/D04PP4Gr7wc4IV_O9... 37 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
25 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/i5-_AceGXQCEEMWU0... 38 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
26 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/rtlxd0zOixA9QHdIZ... 39 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
27 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/N1TrDFLRppxZgBowC... 40 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
28 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/7UMGi6zRySqXopr_r... 41 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
29 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/RScXhi324h8usyIet... 42 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
30 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/0n9MJVCDLpA8q7aiG... 43 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
31 youtube#videoCategory "m2yskBQFythfE4irbTIeOgYYfBU/x5NxSf5fz8hn4loSN... 44 {'channelId': 'UCBR8-60-B28hp2BmDPdntcQ', 'tit...
In [9]:
df1.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 40949 entries, 0 to 40948
Data columns (total 16 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   video_id                40949 non-null  object
 1   trending_date           40949 non-null  object
 2   title                   40949 non-null  object
 3   channel_title           40949 non-null  object
 4   category_id             40949 non-null  int64 
 5   publish_time            40949 non-null  object
 6   tags                    40949 non-null  object
 7   views                   40949 non-null  int64 
 8   likes                   40949 non-null  int64 
 9   dislikes                40949 non-null  int64 
 10  comment_count           40949 non-null  int64 
 11  thumbnail_link          40949 non-null  object
 12  comments_disabled       40949 non-null  bool  
 13  ratings_disabled        40949 non-null  bool  
 14  video_error_or_removed  40949 non-null  bool  
 15  description             40379 non-null  object
dtypes: bool(3), int64(5), object(8)
memory usage: 4.2+ MB
In [10]:
df2.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32 entries, 0 to 31
Data columns (total 4 columns):
 #   Column   Non-Null Count  Dtype 
---  ------   --------------  ----- 
 0   kind     32 non-null     object
 1   etag     32 non-null     object
 2   id       32 non-null     int64 
 3   snippet  32 non-null     object
dtypes: int64(1), object(3)
memory usage: 1.1+ KB
In [11]:
df1.drop(['thumbnail_link'], axis=1, inplace=True)
In [12]:
# Normalize json
snippet = df2['snippet'].apply(lambda a: pd.Series(a))

# alternatives
# snippet = pd.DataFrame(df2['snippet'].tolist())
# from pandas.io.json import json_normalize
# snippet = json_normalize(df2.snippet)

df2 = pd.concat([df2, snippet], axis=1)
In [13]:
# Drop unnecessary columns
df2.drop(['kind', 'etag', 'snippet', 'channelId', 'assignable'], axis=1, inplace=True)
df2.rename(columns = {'title':'category'}, inplace = True)
In [14]:
data = pd.merge(df1,df2,how='inner', left_on='category_id', right_on='id')
data.head()
Out[14]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count comments_disabled ratings_disabled video_error_or_removed description id category
0 2kyS6SvSYSE 17.14.11 WE WANT TO TALK ABOUT OUR MARRIAGE CaseyNeistat 22 2017-11-13T17:13:01.000Z SHANtell martin 748374 57527 2966 15954 False False False SHANTELL'S CHANNEL - https://www.youtube.com/s... 22 People & Blogs
1 0mlNzVSJrT0 17.14.11 Me-O Cats Commercial Nobrand 22 2017-04-21T06:47:32.000Z cute|"cats"|"thai"|"eggs" 98966 2486 184 532 False False False Kittens come out of the eggs in a Thai commerc... 22 People & Blogs
2 STI2fI7sKMo 17.14.11 AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -... Shawn Johnson East 22 2017-11-11T15:00:03.000Z shawn johnson|"andrew east"|"shawn east"|"shaw... 321053 4451 1772 895 False False False Subscribe for weekly videos ▶ http://bit.ly/sj... 22 People & Blogs
3 KODzih-pYlU 17.14.11 BLIND(folded) CAKE DECORATING CONTEST (with Mo... Grace Helbig 22 2017-11-11T18:08:04.000Z itsgrace|"funny"|"comedy"|"vlog"|"grace"|"helb... 197062 7250 217 456 False False False Molly is an god damn amazing human and she cha... 22 People & Blogs
4 8mhTWqWlQzU 17.14.11 Wearing Online Dollar Store Makeup For A Week Safiya Nygaard 22 2017-11-11T01:19:33.000Z wearing online dollar store makeup for a week|... 2744430 115426 1110 6541 False False False I found this online dollar store called ShopMi... 22 People & Blogs
In [15]:
# Convert to Datetime
data.trending_date = pd.to_datetime(data.trending_date, format='%y.%d.%m')
data.publish_time = pd.to_datetime(data.publish_time)
data.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 40949 entries, 0 to 40948
Data columns (total 17 columns):
 #   Column                  Non-Null Count  Dtype              
---  ------                  --------------  -----              
 0   video_id                40949 non-null  object             
 1   trending_date           40949 non-null  datetime64[ns]     
 2   title                   40949 non-null  object             
 3   channel_title           40949 non-null  object             
 4   category_id             40949 non-null  int64              
 5   publish_time            40949 non-null  datetime64[ns, UTC]
 6   tags                    40949 non-null  object             
 7   views                   40949 non-null  int64              
 8   likes                   40949 non-null  int64              
 9   dislikes                40949 non-null  int64              
 10  comment_count           40949 non-null  int64              
 11  comments_disabled       40949 non-null  bool               
 12  ratings_disabled        40949 non-null  bool               
 13  video_error_or_removed  40949 non-null  bool               
 14  description             40379 non-null  object             
 15  id                      40949 non-null  int64              
 16  category                40949 non-null  object             
dtypes: bool(3), datetime64[ns, UTC](1), datetime64[ns](1), int64(6), object(6)
memory usage: 4.8+ MB
In [16]:
# Data without duplicated video, keep the last record of the video
data_clean = data.drop_duplicates(subset ="video_id", keep='last')
len(data_clean)
Out[16]:
6351

Exploratory Data Analysis

In [17]:
cat_cnt = data_clean.category.value_counts().to_frame().reset_index()
cat_cnt.rename(columns={'index':'Category', 'category':'Num_Videos'}, inplace = True)
plt.figure(figsize=(10,8))
sns.barplot(data = cat_cnt, y='Category', x = 'Num_Videos')
plt.xlabel('Number of Videos', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Number of Videos based on Category', fontsize=20)
plt.show()
In [18]:
pct_ent = round(cat_cnt[cat_cnt['Category'] == 'Entertainment'].Num_Videos/len(data_clean)*100,2)[0]
print('{}% of all videos are videos with the entertainment category'.format(pct_ent))
25.46% of all videos are videos with the entertainment category
  • The category that has the most trending videos is entertainment with 1617 videos, followed by music with 801 videos and style with 595 videos.
  • The entertainment category has twice as many trending videos as the music category.
  • The categories that have the least trending videos are shows and Nonprofits/Activism.
  • 25.46% of all videos are videos with the entertainment category
In [19]:
chn_cnt = data_clean.channel_title.value_counts().to_frame().reset_index().head(10)
chn_cnt.rename(columns={'index':'Channel', 'channel_title':'Num_Videos'}, inplace = True)
plt.figure(figsize=(10,8))
sns.barplot(data = chn_cnt, y='Channel', x = 'Num_Videos')

plt.xlabel('Number of Videos', fontsize=20)
plt.ylabel('Channel', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 10 Channel with Most Trending Videos', fontsize=20)
plt.show()
  • based on the top 10 trending videos, there are 6 out of 10 channels in the entertainment category, such as TheEllenhow, The Tonight Show Starring Jimmy Fallon, Jimmi Kimmel Live, etc.
  • Channel with the Most Trending Videos is ESPN (Sports) with 84 Videos, followed by TheEllenhow, and The Tonight Show Starring Jimmy Fallon
In [20]:
data_clean.columns
Out[20]:
Index(['video_id', 'trending_date', 'title', 'channel_title', 'category_id',
       'publish_time', 'tags', 'views', 'likes', 'dislikes', 'comment_count',
       'comments_disabled', 'ratings_disabled', 'video_error_or_removed',
       'description', 'id', 'category'],
      dtype='object')
In [21]:
com_dis = data_clean.comments_disabled.value_counts().to_frame().reset_index().head(10)
com_dis.rename(columns={'index':'boole', 'channel_title':'comments_disabled'}, inplace = True)
com_dis.boole = com_dis.boole.astype('str')
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16, 4))

sns.countplot(x='comments_disabled', data=data_clean, ax=ax1)
# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Comments Disabled', fontsize=15)


sns.countplot(x='ratings_disabled', data=data_clean, ax=ax2)

# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Ratings Disabled', fontsize=15)

sns.countplot(x='video_error_or_removed', data=data_clean, ax=ax3)

# plt.ylabel('Freq', fontsize=15)
# plt.yticks(fontsize=15)
# plt.xticks(fontsize=15)
# plt.title('Video Error or Removed', fontsize=15)
Out[21]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f84bca8d990>
In [21]:

Video Titles

In [22]:
# Just take data type object, and 
data_str = data.select_dtypes('object')
data_str.drop_duplicates(subset ="video_id", keep='first', inplace=True)
data_str
/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  return func(*args, **kwargs)
Out[22]:
video_id title channel_title tags description category
0 2kyS6SvSYSE WE WANT TO TALK ABOUT OUR MARRIAGE CaseyNeistat SHANtell martin SHANTELL'S CHANNEL - https://www.youtube.com/s... People & Blogs
1 0mlNzVSJrT0 Me-O Cats Commercial Nobrand cute|"cats"|"thai"|"eggs" Kittens come out of the eggs in a Thai commerc... People & Blogs
2 STI2fI7sKMo AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -... Shawn Johnson East shawn johnson|"andrew east"|"shawn east"|"shaw... Subscribe for weekly videos ▶ http://bit.ly/sj... People & Blogs
3 KODzih-pYlU BLIND(folded) CAKE DECORATING CONTEST (with Mo... Grace Helbig itsgrace|"funny"|"comedy"|"vlog"|"grace"|"helb... Molly is an god damn amazing human and she cha... People & Blogs
4 8mhTWqWlQzU Wearing Online Dollar Store Makeup For A Week Safiya Nygaard wearing online dollar store makeup for a week|... I found this online dollar store called ShopMi... People & Blogs
... ... ... ... ... ... ...
40883 pwGbwYAfSmg 5 books worth reading this summer Bill Gates Bill|"Gates" If you’re looking for something to read over t... Nonprofits & Activism
40892 lM0yu7c6lQk You're not crazy. Apple is slowing down older ... CNET Apple|"iPhone Slowdown"|"Apple Battery"|"Cultu... Apple comes clean after overwhelming evidence ... Shows
40903 Q1CFfU2gXHw Apple HomePod: Everything to know before you b... CNET Apple|"Homepod"|"smart home"|"Google Home"|"Am... We'll break down all the new Apple HomePod det... Shows
40908 7_FJUSBFbJM Game of Zones - Game of Zones - S5:E1: 'A Gold... Bleacher Report bleacher report|"br"|"nba"|"game of zones"|"go... In the season 5 premiere of Game of Zones, the... Shows
40924 V6ElE2xs48c Game of Zones - S5:E5: The Isle of Van Gundy Bleacher Report bleacher report|"br"|"nba"|"Stan Van Gundy"|"J... On an island full of Van Gundys, Stan gets tal... Shows

6351 rows × 6 columns

In [23]:
## Data Processing

df_title = pd.DataFrame()

# Expanding Contractions
df_title['title_cleaned'] = data_str['title'].apply(lambda x: [contractions.fix(word) for word in x.split()])
df_title['title_cleaned'] = [' '.join(map(str, l)) for l in df_title['title_cleaned']]

# Converting all Characters to Lowercase
df_title['title_cleaned'] = df_title['title_cleaned'].str.lower()

# Remove punctuation
df_title['title_cleaned'] = df_title['title_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
df_title['title_cleaned'] = df_title['title_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
nltk.download('punkt')
df_title["title_tokenized"] = df_title["title_cleaned"].apply(word_tokenize)

df_title.head()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: The default value of regex will change from True to False in a future version.
  del sys.path[0]
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /root/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
Out[23]:
title_cleaned title_tokenized
0 want talk marriage [want, talk, marriage]
1 meo cats commercial [meo, cats, commercial]
2 affairs ex boyfriends 18million net worth goog... [affairs, ex, boyfriends, 18million, net, wort...
3 blindfolded cake decorating contest molly burk... [blindfolded, cake, decorating, contest, molly...
4 wearing online dollar store makeup week [wearing, online, dollar, store, makeup, week]
In [24]:
# collect data into one list
title_words = []
for wordlist in df_title['title_tokenized']:
    title_words += wordlist
print(title_words)
['want', 'talk', 'marriage', 'meo', 'cats', 'commercial', 'affairs', 'ex', 'boyfriends', '18million', 'net', 'worth', 'google', 'us', 'shawn', 'andrew', 'blindfolded', 'cake', 'decorating', 'contest', 'molly', 'burke', 'grace', 'helbig', 'wearing', 'online', 'dollar', 'store', 'makeup', 'week', 'mark', 'ruffalo', 'reads', 'hilarious', 'thirst', 'tweets', 'idiots', 'guide', 'japanese', 'squat', 'toilets', 'russian', 'bots', 'trolls', 'november', '8', '2017', 'act', '2', 'full', 'frontal', 'tbs', 'ultimate', 'red', 'lip', 'look', 'nicole', 'guerriero', 'could', 'anastasia', 'become', 'disney', 'princess', 'mdna', 'skin', 'express', 'picked', 'girlfriends', 'outfit', 'blindfolded', 'love', 'price', 'right', 'wooo', 'kevin', 'see', 'around', 'official', 'audio', 'boyfriend', 'makeup', 'madelaine', 'petsch', 'apple', 'clips', 'sample', 'kids', 'describe', 'america', 'koji', 'illustrator', 'huge', 'beauty', 'favourites', 'autumn', 'loves', 'bill', 'gates', 'bought', 'land', 'arizona', 'build', 'tech', 'city', 'baby', 'loves', 'jeopardy', 'flipping', 'supercar', 'gone', 'wrong', 'granulated', 'sugar', 'honey', 'better', 'roy', 'moore', 'november', '15', '2017', 'act', '1', 'full', 'frontal', 'tbs', 'tried', 'period', 'yoga', 'pants', 'every', 'snack', 'packing', 'victorias', 'secret', 'fashion', 'show', 'karlie', 'kloss', 'taxes', 'complicated', 'musical', 'transparent', 'yas', 'queen', 'eminem', 'speaks', 'working', 'beyonce', 'mtv', 'emas', 'fashion', 'review', 'wkristen', 'mcatee', 'grace', 'helbig', 'ranz', 'niana', 'goes', 'la', 'carpool', 'around', 'quad9', 'install', 'windows', 'dressed', 'like', '1977', 'american', 'influencer', 'awards', '2017', 'greatest', 'best', 'man', 'speech', '10', 'year', 'old', 'son', 'take', 'kids', 'restaurant', 'wild', 'turkey', 'bourbon', 'matthew', 'mcconaughey', 'give', 'back', 'thanksgiving', 'last', 'workout', 'victorias', 'secret', 'fashion', 'show', 'karlie', 'kloss', 'two', 'love', 'stories', 'teaching', 'casey', 'vlog', 'make', 'pumpkin', 'pie', 'mistakes', 'amas', 'fashion', 'review', 'w', 'kingsley', 'grace', 'helbig', 'voices', 'exposing', 'internet', 'search', 'history', 'birth', 'father', 'russia', 'know', 'existed', 'bond', 'whisper', 'challenge', 'w', 'mom', 'grace', 'helbig', 'guessing', 'objects', 'got', 'mail', '4', 'highlights', 'ea', 'predatory', 'behavior', 'announcement', 'youtube', 'back', 'echo', 'show', 'drake', 'virginia', 'black', 'commercial', 'dipclip', 'chicken', 'nugget', 'rally', 'testing', 'secure', 'sauce', 'schizophrenia', 'like', 'make', 'gift', 'box', 'paper', 'easy', 'scene', 'suicide', 'club', '2avi', 'pregnancy', 'announcement', 'two', 'miscarriages', 'baby', 'heart', 'beat', 'mall', 'santa', 'unmasked', 'liam', 'gallagher', 'come', 'back', 'official', 'video', 'let', 'us', 'talk', 'money', 'youtuber', 'quiz', 'truth', 'dare', 'w', 'merrell', 'twins', 'let', 'us', 'talk', 'money', 'everyday', 'smokey', 'eye', 'tutorial', 'cruelty', 'free', 'lucy', 'moon', 'never', 'ever', 'kylie', 'jordyn', 'victoria', 'made', 'jewelry', 'line', 'grace', 'helbig', '5', 'desserts', 'share', 'bff', 'max', 'brenner', 'chocolate', 'bar', 'boyfriend', 'tag', 'w', 'travelaine', 'madelaine', 'petsch', 'behind', 'scenes', 'victorias', 'secret', 'fashion', 'show', 'vlog', 'karlie', 'kloss', 'camera', 'future', 'diy', 'lights', 'paper', 'cut', 'light', 'box', 'art', 'best', 'art', 'ever', 'made', '23', 'feed', 'homeless', 'one', 'list', 'one', 'life', 'michelangelo', 'microsoft', 'excel', 'americans', 'try', 'explain', 'royal', 'family', 'kids', 'describe', 'perfect', 'world', 'koji', 'illustrator', 'try', 'tiny', 'cooking', '30', '30', 'list', 'lucy', 'moon', 'teaching', 'karlie', 'kloss', 'read', 'braille', 'survivalist', 'teaches', 'live', 'grid', 'christmas', 'cupcake', 'tower', 'gone', 'wrong', 'joey', 'graceffa', 'quartz', 'worlds', 'first', 'selfcleaning', 'water', 'bottle', 'skelly', 'skeleton', 'robot', 'wishing', 'merry', 'christmas', 'happy', 'new', 'year', 'lil', 'uzi', 'vert', 'way', 'life', 'goes', 'remix', 'feat', 'nicki', 'minaj', 'official', 'music', 'video', 'got', 'lips', 'permanently', 'tattooed', 'never', 'ever', 'w', 'tyler', 'oakley', 'grace', 'helbig', 'uncomfortable', 'mens', 'fashion', 'tips', 'winter', '2017', 'style', 'guide', 'doctor', 'mike', 'zendaya', 'stops', 'interview', 'fix', 'reporters', 'hair', 'dad', 'hijacks', 'elf', 'shelf', 'greatest', 'tax', 'bill', 'ever', 'sold', 'december', '6', '2017', 'act', '1', 'full', 'frontal', 'tbs', 'surprising', 'facts', 'period', 'finally', 'start', 'building', 'dream', 'home', 'vlogmas', 'day', '6', 'must', 'watch', 'viewer', 'calls', 'news', 'anchor', 'nword', 'live', 'news', 'anchor', 'responds', 'priceless', 'super', 'mario', 'cereal', 'ok', 'feel', 'sad', 'christmas', 'vlogmas', 'day', '7', 'talking', 'fans', 'undercover', 'week', '50', 'facts', 'riverdale', 'set', 'madelaine', 'petsch', 'movies', 'got', 'girlfriend', 'christmas', 'cheerleader', 'invisible', 'box', 'challenge', 'viralhog', 'youtube', 'new', 'york', 'vlogmas', 'no5', 'lucy', 'moon', 'casey', 'neistat', 'merch', 'american', 'christmas', 'box', 'swap', 'vlogmas', 'gift', 'guide', '2017', 'reacting', 'google', 'logos', 'merrell', 'twins', 'live', 'go', 'shoot', 'photography', 'vlog', '137', 'google', 'aiy', 'robot', 'conversion', 'dark', 'skin', 'people', 'get', 'ideal', 'photographs', 'women', 'play', 'hair', 'nah', 'touch', 'black', 'hair', 'artist', 'makes', 'intricate', 'paper', 'cutouts', 'idol', 'exposes', 'dark', 'side', 'kpop', 'storytime', 'experiences', 'scandals', 'slave', 'contracts', 'fake', 'till', 'make', 'tried', 'create', 'christmas', 'tree', 'eyebrows', 'vlogmas', 'day', '4', 'dressed', 'according', 'zodiac', 'sign', 'week', 'rip', 'angry', 'grandpa', 'azealia', 'banks', 'goes', 'remy', 'saying', 'female', 'rap', 'game', 'nothing', 'came', 'home', 'little', 'bit', 'chad', 'jt', 'fight', 'house', 'parties', 'time', 'greatest', 'airplane', 'seat', 'emirates', 'first', 'class', 'suite', '2017', 'best', 'reacting', 'youtube', 'rewind', '2017', 'judge', 'roy', 'moore', 'campaign', 'statement', 'live', 'qa', 'moore', 'december', '13', '2017', 'act', '1', 'full', 'frontal', 'tbs', 'every', 'day', 'official', 'trailer', '2018', 'miracle', 'season', 'official', 'trailer', 'man', 'comforted', '1200', 'newborns', 'winter', 'nighttime', 'routine', 'w', 'riverdale', 'costar', 'vanessa', 'morgan', 'madelaine', 'petsch', 'corgi', 'snowplow', 'lookbook', 'christmas', 'new', 'years', 'eve', 'hannah', 'witton', 'going', 'disneyland', 'first', 'time', 'madelaine', 'petsch', 'brooks', 'guesses', 'makeup', 'prices', 'grace', 'helbig', 'ascend', 'thailands', 'temple', 'rising', 'dragon', 'coin', 'cell', 'challenge', 'animating', 'junk', 'controversial', 'incomplete', 'pass', 'jesse', 'james', 'steelers', 'vs', 'patriots', 'donating', 'lots', 'toys', 'angry', 'grandpa', 'tribute', 'video', 'bought', 'first', '5', 'things', 'insta', 'celebs', 'recommended', 'dogs', 'amazing', 'animation', 'jews', 'try', 'christmas', 'first', 'time', 'dan', 'le', 'batard', 'breaks', 'air', 'john', 'skipper', 'resigns', 'espn', 'psa', 'chairman', 'fcc', 'ajit', 'pai', 'abandoned', 'mall', 'turned', 'winter', 'wonderland', 'nikki', 'bella', 'artem', 'saved', 'last', 'dance', '1', 'million', 'subscribers', 'things', 'leave', '2017', '7', 'amazing', 'diy', 'christmas', 'decorations', 'made', 'pasta', 'history', 'happening', 'cardi', 'b', 'bartier', 'cardi', 'feat', '21', 'savage', 'official', 'audio', 'take', 'sam', 'church', 'december', '20', '2017', 'act', '3', 'full', 'frontal', 'tbs', 'mark', 'hamill', 'luke', 'skywalker', 'biker', 'club', 'helped', 'stop', 'bullying', 'carol', 'bells', 'nyc', 'holiday', 'adventures', 'michael', 'lucie', 'fink', 'vlogs', 'refinery29', 'harvard', 'santa', 'schools', 'yoga', 'challenge', 'w', 'liza', 'koshy', 'joey', 'graceffa', 'making', 'gingerbread', 'train', 'spent', '9', 'months', 'space', 'evacuating', 'flood', 'philippines', 'tropical', 'storm', 'vinta', 'cagayan', 'de', 'oro', 'watch', 'draw', '10', 'presidents', 'queens', 'christmas', 'broadcast', '2017', 'binging', 'babish', 'cooks', 'bobs', 'burgers', 'fans', 'dribbled', 'basketball', 'entire', 'day', 'box', 'challenge', 'amber', 'scholl', 'youtube', 'challenges', 'refinery29', '2017', 'never', 'happened', 'chris', 'brittany', 'italian', 'grandmother', 'learning', 'use', 'google', 'home', 'imac', 'pro', 'new', 'apple', 'store', 'star', 'wars', 'ever', 'really', 'change', '5', 'irish', 'wedding', 'ho', 'ro', 'rattlin', 'bog', 'full', 'face', 'using', 'latina', 'products', 'john', 'cho', 'wild', 'world', 'things', 'need', '2018', 'link', 'japanese', 'samurai', 'real', 'indigo', 'first', 'day', 'year', 'goodbye', '2017', 'diy', 'wireless', 'charger', 'install', 'toyota', 'tacoma', 'bring', '2018', 'tipsy', 'talk', 'anna', 'kendrick', 'anna', 'camp', 'brittany', 'snow', 'zooey', 'citys', 'bag', 'spill', 'refinery29', 'wedding', 'smashed', 'face', 'born', 'shortened', 'limbs', 'one', 'eye', 'geoffs', 'handshake', 'dj', 'khaled', 'shows', 'custom', 'dapper', 'dan', 'gucci', 'jackets', 'made', 'legendary', 'dapper', 'dan', 'introducing', 'new', 'cheetah', 'day', '81', 'safari', 'live', 'girls', 'love', 'cheese', 'alexis', 'sky', 'fetty', 'wap', 'speaks', 'alexis', 'delivering', 'baby', 'premature', '3', 'months', 'early', 'rc', 'jansen', 'strandbeest', 'physical', 'encoder', 'open', 'source', 'braille', 'embosser', 'ready', 'fly', 'thoughts', 'making', 'forbes', 'list', 'mommy', 'moment', 'everyone', 'wearing', 'black', 'golden', 'globes', 'making', 'solar', 'cell', 'almost', 'scratch', 'japan', 'deepfry', 'maple', 'leaves', 'new', 'beginning', 'terrified', 'agents', 'shield', '5x06', 'final', 'scene', 'real', 'life', 'lucky', 'block', 'mystery', 'box', 'country', 'pregnant', 'women', 'january', '10', '2018', 'act', '2', 'full', 'frontal', 'tbs', 'mini', 'supreme', 'pizza', 'domo', 'wilson', 'reacted', 'song', 'reaction', 'video', 'best', '2017', 'kkandbabyj', 'battle', 'boxes', 'important', 'things', 'life', 'vlog²', '129', 'story', 'behind', 'new', 'tattoo', 'mum', 'puts', 'dad', 'january', 'detox', 'jimmy', 'kimmel', 'monologue', 'jan', '11', '2018', 'trump', 'slurs', 'immigrants', 'shthole', 'countries', 'tia', 'mowrys', 'gender', 'reveal', 'quick', 'fix', 'best', 'hairstyle', 'lax', 'bought', 'fake', 'merch', 'lucie', 'fink', 'shares', 'secret', 'nightly', 'routine', 'lucie', 'fink', 'vlogs', 'refinery29', 'eeoc', 'complainants', 'press', 'conference', '5pm', 'jan', '11', '2018', 'single', 'coin', 'strangest', 'booth', 'ces', '2018', 'asthmatic', 'kitty', 'cat', 'getting', 'meds', 'bought', '5', 'knockoff', 'tech', 'products', 'wish', 'keke', 'bossy', 'explicit', 'build', 'self', 'opening', 'door', '20', 'arduino', 'electric', 'motor', 'frog', 'roostersavannah', 'home', 'video', 'shows', 'man', 'trying', 'rob', 'houston', 'cell', 'phone', 'store', 'la', 'de', 'da', 'de', 'da', 'de', 'da', 'de', 'day', 'oh', 'pregnancy', 'body', 'amish', 'town', 'frozen', 'pencil', 'sharpening', 'machine', 'learning', 'youtube', 'w', 'grace', 'helbig', 'mamrie', 'hart', 'half', 'sale', 'called', 'went', 'wearing', 'right', 'top', 'dan', 'memes', '2017', 'kid', 'orders', 'bong', 'package', 'arrives', 'mom', 'wants', 'see', 'open', 'metoo', 'backlash', 'january', '17', '2018', 'act', '1', 'full', 'frontal', 'tbs', 'thanks', 'internet', 'cantina', 'pencil', 'girl', 'turning', 'trixie', 'mattel', 'grace', 'helbig', 'anxiety', 'teens', 'vs', 'adults', 'morgan', 'freeman', 'receives', 'life', 'achievement', 'sag', 'awards', '2018', 'tried', 'cutting', 'hair', 'sword', 'like', 'mulan', 'primitive', 'technology', 'aframe', 'hut', 'sergei', 'eisenstein', 'father', 'montage', 'bros', 'night', 'mates', 'go', 'snow', 'boarding', 'first', 'time', 'end', 'ze', 'world', 'probably', 'real', 'time', 'celebrating', 'terrified', 'introducing', 'lux', 'lipstick', 'usa', 'gymnastics', 'larry', 'nassar', 'disgusted', 'cool', 'hit', 'rhythm', 'stick', 'realtime', 'compositing', 'demo', 'ue4', 'onset', 'facilities', 'test', '1', 'moving', 'beme', 'ever', 'get', 'another', 'relationship', 'qa', 'people', 'try', 'new', 'diet', 'coke', 'flavors', 'helping', 'shane', 'dawson', 'get', 'attitude', 'back', 'riverdale', 'skit', 'madelaine', 'petsch', 'one', 'smallest', 'restaurants', 'new', 'york', 'answering', 'questions', 'avoided', 'something', 'tell', 'bachelor', 'audition', 'tape', 'grace', 'helbig', 'man', 'drops', 'magnum', 'champagne', 'floor', 'ibizas', 'club', 'blackpink', 'intro', '마지막처럼', 'last', '2018', 'seoul', 'music', 'awards', 'gonewe', 'can', 'not', 'without', 'longer', '1', 'year', 'old', 'baby', 'finally', 'gets', 'name', 'vision', 'xfl', '2020', 'height', 'weight', 'bloating', 'new', 'ebook', 'coming', 'soon', 'easy', 'thin', 'japan', 'childish', 'gambino', 'grammy', 'awards', 'performance', '2018', 'kendrick', 'lamar', 'rihanna', 'win', 'best', 'rapsung', 'performance', 'grammy', 'awards', '2018', 'carolyns', 'rv', 'life', 'spots', 'fluffy', 'kitty', 'blythe', 'got', 'makeover', '3', 'different', 'yous', 'cities', 'things', 'changing', 'lularoe', 'consultant', 'mocks', 'special', 'needs', 'hank', 'advice', 'maybe', 'could', 'eat', 'blades', 'grass', 'bullet', 'racing', 'parents', 'react', 'poppy', 'worlds', 'smallest', 'working', 'gamecube', 'controller', 'forced', 'arbitration', 'january', '31', '2018', 'act', '2', 'full', 'frontal', 'tbs', '73', 'questions', 'donatella', 'versace', 'vogue', 'can', 'not', 'stop', 'dreaming', 'waffles', 'michael', 'wolff', 'fire', 'fury', 'theskimm', 'sip', 'n', 'skimm', 'enter', 'japans', 'bizarre', 'museum', 'rocks', 'faces', 'cake', 'decorator', 'vs', 'artist', 'bella', 'twins', 'emotional', 'rollercoaster', 'return', 'ring', 'royal', 'rumble', '2018', 'part', '2', 'diy', 'chipotle', 'bowl', 'live', 'merrell', 'twins', 'conor', 'mcgregor', 'talks', 'floyd', 'mayweather', 'ufc', 'future', 'ronda', 'rousey', '13118', '힐링쿠킹', '빙글빙글', '회오리젤리', '만들기', 'ari', 'kitchen아리키친', 'telephone', 'slipper', 'prank', 'viralhog', 'daughter', 'dogs', 'thought', 'current', 'howl', 'chain', 'funniest', 'local', 'commercial', 'kirk', 'cousins', 'fake', 'donald', 'trump', 'kraft', 'super', 'bowl', 'commercial', '2018', 'family', 'greatly', 'persil', 'proclean', 'gametime', 'staintime', '30', 'rupauls', 'drag', 'race', 'stars', '3', 'episode', '3', 'preview', 'michelob', 'sport', 'super', 'bowl', 'commercial', 'sumo', 'wrestling', '4k', 'small', 'mighty', 'hercules', 'beetle', 'preaztec', 'skeletons', 'found', 'arranged', 'spiral', 'shape', 'national', 'geographic', 'mommy', 'daughter', 'surprise', 'daddy', 'pregnancy', 'announcement', 'speechless', 'migos', 'x', 'tasty', 'whip', 'stir', 'fry', 'taking', 'riverdale', 'quizzes', 'camila', 'mendes', 'hart', 'denton', 'madelaine', 'petsch', 'falcon', 'heavy', 'landing', '02062018', 'hip', 'hop', 'stars', 'get', 'bling', 'woman', 'got', 'fired', 'amateur', 'chef', 'vs', 'professional', 'chef', 'raid', 'fridge', 'challenge', 'week', 'chaos', 'nunes', 'bies', 'february', '7', '2018', 'act', '1', 'pt', '2', 'full', 'frontal', 'tbs', 'cnns', 'alisyn', 'camerota', 'confronts', 'literal', 'nazi', 'running', 'gop', 'congressional', 'seat', 'insane', 'interview', 'northwestern', 'university', 'trombone', 'choir', 'performs', 'national', 'anthem', '2618', 'feels', 'like', 'slide', 'downhill', '90', 'mph', 'logan', 'paul', '98', 'year', 'old', 'dads', 'reaction', 'finds', 'old', 'really', 'warningfoul', 'language', 'martin', 'luther', 'king', 'actually', 'thought', 'car', 'commercials', '10', 'worst', 'british', 'military', 'aircraft', 'get', 'free', 'rv', 'tried', 'fanciest', 'mcdonalds', 'world', 'unboxing', 'video', 'interviewing', 'head', 'youtube', 'business', 'heat', 'grid', 'log', 'cabin', 'wood', 'thermal', 'imaging', 'scan', 'infrared', 'rare', 'stick', 'insect', 'among', 'last', 'kind', 'fbi', 'came', 'visit', 'today', 'nikolas', 'cruz', 'surprising', 'boyfriend', 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'fresh', 'start', 'tour', '50000', 'honda', 'odyssey', 'minivan', 'teslonda', 'action', 'tesla', 'powered', 'honda', 'accord', '360', 'view', 'renault', 'rs18', 'dodging', 'tumbleweeds', 'freeway', 'echo', 'oregon', 'volkswagen', 'gassed', 'monkeys', 'prove', 'diesels', 'clean', 'toyota', 'previa', 'weirdest', 'minivan', 'ever', 'genetic', 'select', 'lexus', 'best', 'wiper', 'blades', 'world', 'expensive', 'cars', 'always', 'reliable', 'tesla', '2018', 'new', 'emirates', 'first', 'class', 'suite', 'boeing', '777', 'emirates', 'lightning', 'strike', 'b777300', 'departure', 'kl743', 'phbvs', 'vostok', 'brompton', 'beach', 'ride', 'inside', 'worlds', 'private', 'boeing', '787', 'dreamliner', 'horrors', 'flavored', 'water', 'brazils', 'video', 'game', 'gray', 'markets', 'indian', 'street', 'food', 'dreams', 'kolkata', 'india', 'enter', 'curry', 'heaven', 'best', 'street', 'food', 'india', 'train', 'runs', 'thailands', 'dangerous', 'market', 'mesmerizing', 'shockwaves', 'window', 'screen', '18000fps', 'beyond', 'slow', 'motion', 'flying', 'sunset', 'storm', 'viralhog', '70', 'people', 'try', '70', 'tonguetwisters', '70', 'countries', 'condé', 'nast', 'traveler', 'donald', 'trump', 'audioanimatronic', 'figure', 'new', 'hall', 'presidents', 'first', 'look', 'donald', 'trump', 'animatronic', 'debuts', 'hall', 'presidents', 'walt', 'disney', 'world', 'beautiful', 'restaurant', 'japan', 'kyoto', 'kitcho', 'thank', 'paloma', 'manhattan', 'express', 'big', 'apple', 'roller', 'coaster', '4k', '60fps', 'nyny', 'las', 'vegas', 'hotel', 'casino', 'front', 'seat', 'pov', 'completely', 'spillproof', 'travel', 'bag', '70', 'people', '70', 'countries', 'imitate', 'americans', 'condé', 'nast', 'traveler', 'amazing', 'canadian', 'food', 'insanely', 'huge', 'christmas', 'turkey', 'dinner', 'feast', 'eggs', 'breakfast', 'canada', 'indian', 'street', 'food', 'tour', 'deep', 'punjab', 'india', 'best', 'street', 'food', 'india', 'best', 'curry', 'heaven', 'kitchen', 'gadgets', 'perfectionists', 'polish', 'girl', 'buys', 'sail', 'boat', '1', 'yes', 'one', 'euro', 'ep', '122', 'best', 'pho', 'traditional', 'noodle', 'tour', 'saigon', 'vietnam', 'continental', 'divide', 'trail', 'four', 'minutes', 'guinness', 'world', 'record', 'worlds', 'largest', 'aerial', 'firework', 'ojo', 'commuter', 'scooter', 'light', 'electric', 'vehicle', 'bike', 'lane', 'beyond', 'las', 'best', 'indian', 'food', 'gas', 'station', 'visit', 'immense', 'reallife', 'fairytale', 'castle', 'national', 'geographic', 'toaster', 'cooks', 'entire', 'breakfast', 'fresh', 'market', 'meat', 'cheese', 'extravaganza', 'rome', 'italy', 'trump', 'sthole', 'countries', 'shithole', 'statement', 'namibia', '360', 'eruption', 'volcano', 'klyuchevskaya', 'sopka', 'kamchatka', 'russia', '4k', 'aerial', 'video', 'tourism', 'australia', 'dundee', 'super', 'bowl', 'ad', '2018', 'w', 'chris', 'hemsworth', 'danny', 'mcbride', 'turkish', 'airlines', '5', 'senses', 'dr', 'oz', 'insane', 'spacex', 'falcon', 'heavy', 'side', 'boosters', 'landing', 'simultaneously', 'kennedy', 'space', 'center', 'kimchi', 'made', 'foreigner', 'surprising', 'indians', 'hindi', 'warning', 'smiles', 'galore', 'disneyparkslive', 'super', 'bowl', 'celebration', 'walt', 'disney', 'world', 'extreme', 'mexican', 'street', 'food', 'blood', 'cactus', 'tacos', 'spicy', 'street', 'market', 'taco', 'tour', 'mexico', 'city', 'god', 'level', 'street', 'food', 'mexico', 'sandwich', 'ninja', 'super', 'fast', 'cutting', 'skills', 'mexican', 'chicken', 'eating', 'brunch', 'taiwan', '7eleven', 'toy', 'story', 'land', 'open', 'walt', 'disney', 'world', 'resort', 'june', '30', 'krispy', 'kreme', 'doughnuts', 'made', 'wtf', '300', 'toaster', 'nail', 'polish', 'made', 'tested', '8', 'potters', 'shaping', 'pottery', 'game', 'legendary', 'eat', 'buffet', 'manila', 'philippines', 'spiral', 'buffet', 'review', 'worlds', 'darkest', 'building', '2018', 'olympics', 'mexican', 'street', 'food', 'tour', 'oaxaca', 'mexico', 'best', 'street', 'food', 'mexico', 'huge', 'mexican', 'breakfast', 'slightly', 'exaggerated', 'travel', 'oregon', 'spring', 'looks', 'like', 'around', 'world', '70', 'people', '70', 'countries', 'sing', 'countrys', 'national', 'anthem', 'condé', 'nast', 'traveler', 'wagyu', 'sandwich', 'worth', '85', 'creating', 'pandora', 'conservation', 'initiative', 'utility', 'suit', 'luxurious', 'eat', 'buffet', 'mumbai', 'india', 'tiny', 'food', 'videos', 'made', '70', 'people', 'reveal', 'tell', 'someone', 'country', 'condé', 'nast', 'traveler', 'amazing', 'eat', 'bbq', 'kebab', 'buffet', 'india', 'eating', 'dad', 'la', 'noire', 'nintendo', 'switch', 'trailer', 'first', 'date', 'playing', 'cuphead', 'matpat', 'mega', 'medley', 'supposed', '5', 'minutes', 'chuck', 'norris', 'hunter', 'world', 'warcraft', 'tv', 'commercial', '2011', 'pokémon', 'challenge', 'watch', 'game', 'freaks', 'kazumasa', 'iwao', 'guess', 'pokémon', 'test', 'footage', 'raspberry', 'pi', 'camera', 'dark', 'pokémon', 'go', 'pokémon', 'adventure', 'dynamic', 'weather', 'gameplay', 'valley', 'gods', 'announcement', 'trailer', 'youtube', 'rewind', '2017', 'behind', 'scenes', 'game', 'awards', 'full', 'show', 'death', 'stranding', 'zelda', 'legend', 'zelda', 'breath', 'wild', 'expansion', 'pass', 'dlc', 'pack', '2', 'champions', 'ballad', 'trailer', 'playerunknowns', 'battlegrounds', 'game', 'awards', '2017', 'gameplay', 'trailer', 'secret', 'fan', 'fictions', 'truth', 'bombs', '2', 'legend', 'zelda', 'breath', 'wild', 'expansion', 'pass', 'champions', 'ballad', 'trailer', 'music', 'splatoon', '2', 'bts', 'nintendo', 'switch', 'playerunknowns', 'battlegrounds', 'xbox', 'action', 'trailer', 'developer', 'update', 'happy', 'new', 'year', 'overwatch', 'nintendo', 'switch', 'fastestselling', 'home', 'console', 'us', 'history', 'ign', 'news', 'matt', 'radar', 'technician', 'star', 'wars', 'battlefront', '2', 'new', 'battlefront', '2', 'mod', 'gameplay', 'youtube', 'sent', 'mystery', 'gift', 'nintendo', 'direct', 'mini', '1112018', 'dunkeys', 'best', '2017', 'aliens', 'rideplanet', 'coaster', 'solve', 'mysteries', 'detective', 'pikachu', 'big', 'sister', 'reflexes', 'fair', 'maiden', 'epic', 'npc', 'man', 'vldl', 'first', 'look', 'nintendo', 'labo', 'look', 'turned', 'ft', 'james', 'charles', 'nintendo', 'labo', 'adults', 'okay', 'beat', 'saber', 'gameplay', 'teaser', 'god', 'war', 'story', 'trailer', 'ps4', 'dragon', 'ball', 'fighterz', 'video', 'review', 'melix', 'plays', 'jumpstart', 'first', 'grade', '90s', 'games', 'fifa', '18', 'fut', 'champions', 'cup', 'barcelona', 'day', '1', 'swiss', 'group', 'round', '3', 'download', 'january', '2018', 'american', 'vs', 'brit', 'sega', 'game', 'gear', 'commercial', 'creamed', 'spinach', 'retro', 'video', 'game', 'commercial', 'ad', '42', 'history', 'super', 'mario', 'bros', 'infamous', 'level', 'camera', 'secrets', 'grand', 'theft', 'auto', 'v', 'boundary', 'break', 'amazing', 'creatures', 'discovered', 'across', 'planet', 'getting', 'spoilers', 'legend', 'zelda', 'breath', 'wild', 'dlc', 'dev', 'talk', 'ft', 'mr', 'aonuma', 'mr', 'fujibayashi', 'nintendo', 'labo', 'handson', 'kaz', 'hirai', 'stepping', 'sony', 'ceo', 'ign', 'news', 'developer', 'update', 'happy', 'year', 'dog', 'overwatch', 'best', 'pirate', 'ever', 'seen', 'altos', 'odyssey', 'trailer', 'launching', 'february', '22nd', 'preorder', '2018', 'bugatti', 'chiron', 'forza', 'motorsport', '7', 'lotto', 'powerball', 'imagine', 'armoured', 'truck', 'got', 'catch', 'nintendo', 'switch', '1', 'year', 'anniversary', 'dev', 'talk', 'ft', 'mr', 'takahashi', 'satellites', 'capture', '400', 'megapixel', 'images', 'earths', 'globe', 'himawari', '8', 'goes16', 'nintendo', 'direct', '382018', 'official', 'call', 'duty', 'black', 'ops', '4', 'teaser', 'super', 'smash', 'bros', 'switch', 'hiding', 'flames', 'realtime', 'character', 'analysis', 'super', 'smash', 'bros', 'trailer', 'analysis', 'sonic', 'mania', 'adventures', 'sneak', 'peek', 'irelia', 'blade', 'dancer', 'champion', 'trailer', 'league', 'legends', 'nintendo', 'switch', 'nindies', 'showcase', 'spring', '2018', 'surprising', 'new', 'puppy', '8', 'weeks', 'old', 'chicas', 'birthday', 'meet', 'dog', 'animated', 'leeroy', 'jenkins', 'animated', 'overwatch', 'seasonal', 'event', 'overwatch', 'archives', 'game', 'theory', 'fortnite', 'meteor', 'destroy', 'everything', 'fortnite', 'battle', 'royale', 'markiplier', 'animated', 'ghost', 'hunter', 'guys', 'get', 'finally', 'meet', 'special', 'announcement', 'transforming', 'homeless', 'womans', 'life', 'extremely', 'emotional', 'boogiedown', 'contest', 'winners', 'announced', 'clash', 'royale', 'clan', 'wars', 'season', '4', 'announce', 'trailer', 'ghost', 'pepper', 'day', '2', 'nintendo', 'labo', 'introducing', 'players', 'super', 'smash', 'bros', 'invitational', '2018', 'clash', 'royale', 'meet', 'rascals', 'new', 'card', 'playing', 'deadpool', 'ryan', 'reynolds', 'official', 'call', 'duty', 'black', 'ops', '4', 'multiplayer', 'reveal', 'trailer', 'lie', 'detector', 'test', 'girlfriend', 'game', 'theory', 'rich', 'pokemon', 'master', 'pranked', 'bought', 'dream', 'car', 'emotional', 'surprise', 'sims', '4', 'seasons', 'official', 'reveal', 'trailer', 'history', 'fortnite', 'battle', 'royale', 'know', 'gaming', 'feat', 'remix', 'pokémon', 'let', 'us', 'go', 'pikachu', 'pokémon', 'let', 'us', 'go', 'eevee', 'trailer', 'try', 'slip', 'n', 'slide', 'wrong', 'mystery', 'box', 'game', 'theory', 'fnaf', 'stumped', 'fnaf', '6', 'ultimate', 'custom', 'night', 'anthem', 'official', 'cinematic', 'trailer', '2018', 'battlefield', '5', 'official', 'multiplayer', 'trailer', 'madden', 'nfl', '19', 'official', 'reveal', 'trailer', 'game', 'theory', 'fnaf', 'theory', 'changed', 'everything', 'fnaf', '6', 'ultimate', 'custom', 'night', 'youtube', 'live', 'e3', '2018', 'monday', 'ninja', 'marshmello', 'playstation', 'ubisoft', 'todd', 'howard', 'town', 'hall', '12', 'update', 'clash', 'clans', 'official', 'elder', 'scrolls', 'vi', 'official', 'e3', 'announcement', 'teaser', 'cyberpunk', '2077', 'official', 'e3', '2018', 'trailer', 'kingdom', 'hearts', 'iii', 'e3', '2018', 'frozen', 'trailer', 'cuphead', 'dlc', 'announcement', 'trailer', 'xbox', 'one', 'windows', '10', 'steam', 'gog', 'fallout', '76', 'trailer', 'e3', '2018', 'halo', 'infinite', 'e3', '2018', 'announcement', 'trailer', 'nintendo', 'e3', '2018', 'day', '1', 'super', 'smash', 'bros', 'ultimate', 'everyone', 'trailer', 'e3', '2018', 'assassins', 'creed', 'odyssey', 'e3', '2018', 'official', 'world', 'premiere', 'trailer', 'ubisoft', 'na', 'nintendo', 'e3', '2018', 'day', '2', 'fortnite', 'celebrity', 'proam', 'fortniteproam', 'tell', 'hollywood', 'stand', 'immigrants', 'nsc', 'face', 'face', 'prescription', 'opioid', 'crisis', 'meet', 'teen', 'vogues', '2017', '21', '21', 'teen', 'vogue', 'living', 'bridge', 'stop', 'homeless', 'woman', 'staying', 'positive', 'nina', 'dobrev', 'wants', 'save', 'sharks', '30s', 'homeless', 'little', 'boy', 'gets', 'bed', 'christmas', 'obama', 'presidential', 'center', 'suicide', 'tomorrow', 'rose', 'mcgowan', 'shares', 'thoughts', 'times', 'movement', 'view', 'unrigged', 'live', 'jennifer', 'lawrence', 'tig', 'notaro', 'richard', 'painter', 'saving', 'lives', 'lead', 'overpopulation', 'яхты', 'олигархи', 'девочки', 'охотница', 'на', 'мужчин', 'разоблачает', 'взяточника', '2018', 'writers', 'guild', 'awards', 'amber', 'ruffin', 'opening', 'monologue', '5', 'books', 'worth', 'reading', 'summer', 'crazy', 'apple', 'slowing', 'older', 'iphones', 'apple', 'byte', 'apple', 'homepod', 'everything', 'know', 'buy', 'apple', 'byte', 'game', 'zones', 'game', 'zones', 's5e1', 'golden', 'summer', 'game', 'zones', 's5e5', 'isle', 'van', 'gundy']
In [25]:
# Word Cloud in titles
mostcommon = FreqDist(title_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Words', fontsize=30)
plt.tight_layout(pad=0)
plt.show()
In [26]:
mostcommon_small = FreqDist(title_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Words', fontsize=20)
plt.show()
  • Based on chart and word cloud we can see that the word that appears the most is "official" followed by "2018" and "video". this is because 65% of videos uploaded in 2018
In [27]:
# Bigram Data
tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_title['title_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]
In [28]:
mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [29]:
mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Bigrams', fontsize=20)
plt.show()
In [30]:
tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_title['title_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]
In [31]:
mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [32]:
mostcommon_small = FreqDist(ng3).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,5))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Trigrams', fontsize=20)
plt.ylabel('Frequency of Trigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Trigrams', fontsize=20)
plt.show()
  • Based on Bigrams and Trigrams, we can conclude that the most combinations of words focus on film trailers and music videos, for example, the films Star Wars: The Last Jedi and Avengers: Infinity Wars.

Video Description

In [33]:
df_desc = pd.DataFrame()

# Expanding Contractions
df_desc['desc_cleaned'] = data_str['description'].dropna().apply(lambda x: [contractions.fix(word) for word in x.split()])
df_desc['desc_cleaned'] = [' '.join(map(str, l)) for l in df_desc['desc_cleaned']]

# Converting all Characters to Lowercase
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.lower()

# replace . and / from the link in the description
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace('.', ' ')
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace('/', ' ')

# Remove punctuation
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
df_desc['desc_cleaned'] = df_desc['desc_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
df_desc["desc_tokenized"] = df_desc["desc_cleaned"].apply(word_tokenize)

df_desc.head()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  # This is added back by InteractiveShellApp.init_path()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: The default value of regex will change from True to False in a future version.
  from ipykernel import kernelapp as app
Out[33]:
desc_cleaned desc_tokenized
0 shantells channel https www youtube com shante... [shantells, channel, https, www, youtube, com,...
1 kittens come eggs thai commercialnnreuploaded ... [kittens, come, eggs, thai, commercialnnreuplo...
2 subscribe weekly videos http bit ly sjofficoak... [subscribe, weekly, videos, http, bit, ly, sjo...
3 molly god damn amazing human challenged blind ... [molly, god, damn, amazing, human, challenged,...
4 found online dollar store called shopmissa sel... [found, online, dollar, store, called, shopmis...
In [34]:
desc_words = []
for wordlist in df_desc['desc_tokenized']:
    desc_words += wordlist
# print(desc_words)
In [35]:
# Word Cloud in titles
mostcommon = FreqDist(desc_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Words', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [36]:
mostcommon_small = FreqDist(desc_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Words', fontsize=20)
plt.show()
In [37]:
tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_desc['desc_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]
In [38]:
mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [39]:
mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Bigrams', fontsize=20)
plt.show()
In [40]:
tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_desc['desc_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]
In [41]:
mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=1600, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(20,8), facecolor='white')
plt.imshow(wordcloud, interpolation='lanczos')
plt.axis('off')
plt.title('Top 50 Most Common Trigrams', fontsize=50)
plt.tight_layout(pad=0)
plt.show()
In [42]:
mostcommon_small = FreqDist(ng3).most_common(25)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(20,10))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Trigrams', fontsize=20)
plt.show()
  • In the video description, most users include their social media links such as Google, Facebook, Instagram, and Twitter or other things that require links, for example download links and product links. Another word that is pretty much listed in the description is a request to subscribe to the channel

Video Tags

In [43]:
df_tags = pd.DataFrame()

# Expanding Contractions
df_tags['tags_cleaned'] = data_str['tags'].dropna().apply(lambda x: [contractions.fix(word) for word in x.split()])
df_tags['tags_cleaned'] = [' '.join(map(str, l)) for l in df_tags['tags_cleaned']]

# Converting all Characters to Lowercase
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.lower()

# Remove punctuation
# df_tags['desc_cleaned'] = df_tags['desc_cleaned'].str.replace('.', ' ')
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.replace('|', ' ')
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].str.replace(r'[^\w\s]+', '')

# Remove stopwords
df_tags['tags_cleaned'] = df_tags['tags_cleaned'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))

# Tokenization
df_tags["tags_tokenized"] = df_tags["tags_cleaned"].apply(word_tokenize)

df_tags.head()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  if sys.path[0] == '':
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: The default value of regex will change from True to False in a future version.
  del sys.path[0]
Out[43]:
tags_cleaned tags_tokenized
0 shantell martin [shantell, martin]
1 cute cats thai eggs [cute, cats, thai, eggs]
2 shawn johnson andrew east shawn east shawn and... [shawn, johnson, andrew, east, shawn, east, sh...
3 itsgrace funny comedy vlog grace helbig graceh... [itsgrace, funny, comedy, vlog, grace, helbig,...
4 wearing online dollar store makeup week online... [wearing, online, dollar, store, makeup, week,...
In [44]:
tags_words = []
for wordlist in df_tags['tags_tokenized']:
    tags_words += wordlist
# print(tags_words)
In [45]:
# Word Cloud in titles
mostcommon = FreqDist(tags_words).most_common(100)
wordcloud = WordCloud(width=800, height=800, background_color='white').generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 100 Most Common Tags', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [46]:
mostcommon_small = FreqDist(tags_words).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Tags', fontsize=20)
plt.ylabel('Frequency of Tags', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 20 Most Common Tags', fontsize=20)
plt.show()
In [47]:
tokenizer = RegexpTokenizer(r"[\w']+")
ng2 = [ngrams(tokenizer.tokenize(t.lower()), n=2) for t in df_tags['tags_cleaned']]

ng2 = [x for y in ng2 for x in y]
ng2 = [x + '-' + y for x,y in ng2]
In [48]:
mostcommon = FreqDist(ng2).most_common(50)
wordcloud = WordCloud(width=800, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(10,10), facecolor='white')
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.title('Top 50 Most Common Bigrams', fontsize=40)
plt.tight_layout(pad=0)
plt.show()
In [49]:
mostcommon_small = FreqDist(ng2).most_common(20)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(15,6))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Bigrams', fontsize=20)
plt.ylabel('Frequency of Bigrams', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Bigrams', fontsize=20)
plt.show()
In [50]:
tokenizer = RegexpTokenizer(r"[\w']+")
ng3 = [ngrams(tokenizer.tokenize(t.lower()), n=3) for t in df_tags['tags_cleaned']]
ng3 = [x for y in ng3 for x in y]
ng3 = [x + '-' + y + '-' + z for x,y,z in ng3]
In [51]:
mostcommon = FreqDist(ng3).most_common(50)
wordcloud = WordCloud(width=1600, height=800, background_color='white', normalize_plurals=False, regexp=r".+").generate_from_frequencies(dict(mostcommon))
fig = plt.figure(figsize=(20,8), facecolor='white')
plt.imshow(wordcloud, interpolation='lanczos')
plt.axis('off')
plt.title('Top 50 Most Common Trigrams', fontsize=50)
plt.tight_layout(pad=0)
plt.show()
In [52]:
mostcommon_small = FreqDist(ng3).most_common(25)
x, y = zip(*mostcommon_small)
plt.figure(figsize=(20,10))
plt.margins(0.02)
plt.bar(x, y)
plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Frequency of 25 Most Common Trigrams', fontsize=20)
plt.show()

Video Comments

In [53]:
data_clean['comment_count'].describe()
Out[53]:
count    6.351000e+03
mean     6.450380e+03
std      3.382373e+04
min      0.000000e+00
25%      3.720000e+02
50%      1.266000e+03
75%      4.061500e+03
max      1.361580e+06
Name: comment_count, dtype: float64

Video Comments Distribution

In [54]:
ax = data_clean['comment_count'].plot(kind='hist', bins=np.arange(0, 1.5e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Comments', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Comments Count Distribution', fontsize=20)
plt.show()
In [55]:
data_clean.sort_values('comment_count', ascending = False)
plt.figure(figsize=(10,5))
ax = sns.barplot(data = data_clean.sort_values('comment_count', ascending = False).head(5),
            x = 'comment_count',
            y = 'title')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Comments', fontsize=20)
plt.ylabel('Title', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 5 Trending Videos Based on Number of Comments', fontsize=20)
plt.show()
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 53444 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49548 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45380 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45800 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 53444 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49548 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45380 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45800 missing from current font.
  font.set_text(s, 0, flags=flags)
In [56]:
data_clean[['title', 'channel_title', 'category', 'publish_time',  'comment_count']].sort_values('comment_count', ascending = False).head(5)
Out[56]:
title channel_title category publish_time comment_count
5912 So Sorry. Logan Paul Vlogs Entertainment 2018-01-02 16:42:21+00:00 1361580
31839 BTS (방탄소년단) 'FAKE LOVE' Official MV ibighit Music 2018-05-18 09:00:02+00:00 1228655
4703 YouTube Rewind: The Shape of 2017 | #YouTubeRe... YouTube Spotlight Entertainment 2017-12-06 17:58:51+00:00 810698
40865 Suicide: Be Here Tomorrow. Logan Paul Vlogs Nonprofits & Activism 2018-01-24 18:30:01+00:00 658130
26673 BTS (방탄소년단) 'MIC Drop (Steve Aoki Remix)' Offi... ibighit Music 2017-11-24 09:00:02+00:00 546100

Comments Distribution by Category

In [57]:
data_clean['likes_log'] = np.log(data_clean['likes'] + 1)
data_clean['views_log'] = np.log(data_clean['views'] + 1)
data_clean['dislikes_log'] = np.log(data_clean['dislikes'] + 1)
data_clean['comment_log'] = np.log(data_clean['comment_count'])

plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="comment_log", data=data_clean)

plt.xlabel('Words', fontsize=20)
plt.ylabel('Frequency of Words', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Comments Distribution by Category', fontsize=20)
plt.show()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log
  result = getattr(ufunc, method)(*inputs, **kwargs)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  after removing the cwd from sys.path.
In [58]:
plt.figure(figsize=(10,8))
ax = sns.barplot(data = data_clean.groupby('category').agg({'comment_count':'sum'}).reset_index(), y='category', x='comment_count')

plt.xlabel('Comment Count', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Number of Comment Count by Category', fontsize=20)
plt.show()
In [59]:
data_clean[data_clean['comment_count'] < 100000].shape[0] / data_clean.shape[0] * 100
Out[59]:
99.2757046134467
In [60]:
data_clean['comment_count'].quantile(q=0.9)
Out[60]:
11775.0
  • From the distribution we can see that the majority of trending videos have less than 100k comments. 99.27% of trending videos have less than 100k comments and 90% of trending videos have less than 11,775 comments.

  • We can notice that only two videos that have more than 1 million comments, BTS 'Fake Love' Music Video and So Sorry from Logan Paul.

  • Gaming is the category with the highest average number of comments followed by Music, Comedy, and Shows.

Video Likes

In [61]:
data_clean['likes'].describe()
Out[61]:
count    6.351000e+03
mean     5.557323e+04
std      1.938312e+05
min      0.000000e+00
25%      2.771000e+03
50%      1.190600e+04
75%      3.842850e+04
max      5.613827e+06
Name: likes, dtype: float64
In [62]:
ax = data_clean['likes'].plot(kind='hist', bins=np.arange(0, 6e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Likes', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Likes Distribution', fontsize=20)
plt.show()
In [63]:
plt.figure(figsize=(10,5))
ax = sns.barplot(data = data_clean.sort_values('likes', ascending = False).head(5),
            x = 'likes',
            y = 'title')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Likes', fontsize=20)
plt.ylabel('Title', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 5 Videos with Most Likes', fontsize=20)
plt.show()
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 53444 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49548 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45380 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45800 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49548 missing from current font.
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/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45800 missing from current font.
  font.set_text(s, 0, flags=flags)
In [64]:
data_clean[['title', 'channel_title', 'category', 'publish_time',  'likes']].sort_values('likes', ascending = False).head(5)
Out[64]:
title channel_title category publish_time likes
31839 BTS (방탄소년단) 'FAKE LOVE' Official MV ibighit Music 2018-05-18 09:00:02+00:00 5613827
31902 Childish Gambino - This Is America (Official V... ChildishGambinoVEVO Music 2018-05-06 04:00:07+00:00 5023450
31170 Ariana Grande - No Tears Left To Cry ArianaGrandeVevo Music 2018-04-20 04:00:03+00:00 3094021
4703 YouTube Rewind: The Shape of 2017 | #YouTubeRe... YouTube Spotlight Entertainment 2017-12-06 17:58:51+00:00 3093544
26673 BTS (방탄소년단) 'MIC Drop (Steve Aoki Remix)' Offi... ibighit Music 2017-11-24 09:00:02+00:00 2729292

Likes Distribution by Category

In [65]:
plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="likes_log", data=data_clean)

plt.xlabel('Likes (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Likes Distribution by Category', fontsize=20)
plt.show()
In [66]:
data_clean[data_clean['likes'] < 100000].shape[0] / data_clean.shape[0] * 100
Out[66]:
88.33254605573926
In [67]:
data_clean['likes'].quantile(q=0.9)
Out[67]:
117929.0
  • 98.33% of trending videos have less than 500k likes

  • Videos with largest number of likes is BTS 'Fake Love' Music Video followed by Childish Gambino 'This Is America' Music Video.

  • Music and Gaming is the category with the highest average number of likes

Video Dislikes

In [68]:
data_clean['dislikes'].describe()
Out[68]:
count    6.351000e+03
mean     3.067025e+03
std      3.198850e+04
min      0.000000e+00
25%      1.270000e+02
50%      4.440000e+02
75%      1.507000e+03
max      1.674420e+06
Name: dislikes, dtype: float64
In [69]:
ax = data_clean['dislikes'].plot(kind='hist', bins=np.arange(0, 1.8e6, 1e5), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Likes', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Dislikes Distribution', fontsize=20)
plt.show()
In [70]:
plt.figure(figsize=(10,5))
ax = sns.barplot(data = data_clean.sort_values('dislikes', ascending = False).head(5),
            x = 'dislikes',
            y = 'title')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}K'.format(x/1e3))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of dislikes', fontsize=20)
plt.ylabel('Title', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 5 Videos with Most Dislikes', fontsize=20)
plt.show()
In [71]:
data_clean[['title', 'channel_title', 'category', 'publish_time', 'dislikes']].sort_values('dislikes', ascending = False).head(5)
Out[71]:
title channel_title category publish_time dislikes
5912 So Sorry. Logan Paul Vlogs Entertainment 2018-01-02 16:42:21+00:00 1674420
4703 YouTube Rewind: The Shape of 2017 | #YouTubeRe... YouTube Spotlight Entertainment 2017-12-06 17:58:51+00:00 1643059
40865 Suicide: Be Here Tomorrow. Logan Paul Vlogs Nonprofits & Activism 2018-01-24 18:30:01+00:00 497847
31902 Childish Gambino - This Is America (Official V... ChildishGambinoVEVO Music 2018-05-06 04:00:07+00:00 343541
7238 LOGAN PAUL IS BACK! Logan Paul Vlogs Entertainment 2018-02-04 22:02:27+00:00 291900

Dislikes Distribution by Category

In [72]:
plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="dislikes_log", data=data_clean)

plt.xlabel('Dislikes (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Dislikes Distribution by Category', fontsize=20)
plt.show()
In [73]:
data_clean[data_clean['dislikes'] < 10000].shape[0] / data_clean.shape[0] * 100
Out[73]:
95.6857187844434
In [74]:
data_clean['dislikes'].quantile(q=0.9)
Out[74]:
4514.0
  • 95.68% of trending videos have less than 10k dislikes

  • Videos with largest number of likes is So Sorry from Logan Paul followed by Youtube Rewind 2017.

Video Views

In [75]:
data_clean['views'].describe()
Out[75]:
count    6.351000e+03
mean     1.962023e+06
std      7.060074e+06
min      5.590000e+02
25%      1.589615e+05
50%      5.181070e+05
75%      1.473582e+06
max      2.252119e+08
Name: views, dtype: float64
In [76]:
ax = data_clean['views'].plot(kind='hist', bins=np.arange(0, 2.3e8, 1e7), figsize=(10,5), logy=True)

formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Frequency', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()
In [77]:
plt.figure(figsize=(10,5))
ax = sns.barplot(data = data_clean.sort_values('views', ascending = False).head(5),
            x = 'views',
            y = 'title')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.0f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Title', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Top 5 Videos with Most Views', fontsize=20)
plt.show()
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 48169 missing from current font.
  font.set_text(s, 0.0, flags=flags)
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  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49548 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45380 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45800 missing from current font.
  font.set_text(s, 0.0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 48169 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 53444 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49548 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45380 missing from current font.
  font.set_text(s, 0, flags=flags)
/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45800 missing from current font.
  font.set_text(s, 0, flags=flags)
In [78]:
data_clean[['title', 'channel_title', 'category', 'publish_time',  'views']].sort_values('views', ascending = False).head(5)
Out[78]:
title channel_title category publish_time views
31902 Childish Gambino - This Is America (Official V... ChildishGambinoVEVO Music 2018-05-06 04:00:07+00:00 225211923
4703 YouTube Rewind: The Shape of 2017 | #YouTubeRe... YouTube Spotlight Entertainment 2017-12-06 17:58:51+00:00 149376127
31170 Ariana Grande - No Tears Left To Cry ArianaGrandeVevo Music 2018-04-20 04:00:03+00:00 148689896
31207 Becky G, Natti Natasha - Sin Pijama (Official ... BeckyGVEVO Music 2018-04-20 10:40:51+00:00 139334502
31839 BTS (방탄소년단) 'FAKE LOVE' Official MV ibighit Music 2018-05-18 09:00:02+00:00 123010920

Views Distribution by Category

In [79]:
plt.figure(figsize=(8,10))
ax = sns.boxplot(y="category", x="views_log", data=data_clean)

plt.xlabel('Views (log)', fontsize=20)
plt.ylabel('Category', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(rotation=60, fontsize=20)
plt.title('Views Distribution by Category', fontsize=20)
plt.show()
In [80]:
data_clean[data_clean['views'] < 518107].shape[0] / data_clean.shape[0] * 100
Out[80]:
49.9921272240592
In [81]:
data_clean['views'].quantile(q=0.80)
Out[81]:
1888088.0
  • 92.89% of trending videos have less than 5 million views

  • Videos with largest number of views is Childish Gambino 'This Is America' Music Video, with 225 Million Views

  • Gaming is the category with the highest average number of likes, followed by Music

Relationship

In [82]:
data_clean.columns
Out[82]:
Index(['video_id', 'trending_date', 'title', 'channel_title', 'category_id',
       'publish_time', 'tags', 'views', 'likes', 'dislikes', 'comment_count',
       'comments_disabled', 'ratings_disabled', 'video_error_or_removed',
       'description', 'id', 'category', 'likes_log', 'views_log',
       'dislikes_log', 'comment_log'],
      dtype='object')
In [83]:
plt.figure(figsize=(10,10))

sns.heatmap(data_clean[['views', 'likes', 'dislikes', 'comment_count']].corr(), annot = True, annot_kws={'fontsize':20}, cbar_kws={"shrink": 0.9})
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Heatmap Correlation', fontsize=20)
plt.show()
In [84]:
plt.figure(figsize=(14,8))
ax = sns.regplot(data = data_clean, x='views', y='likes', scatter_kws={'alpha':0.5})
# plt.figure(figsize=(14,8))
# plt.plot(data_clean['views'], data_clean['likes'], 'o',markersize=10, alpha=0.2)
# plt.axis([0,0.2e8,0,0.6e6])
# plt.xlabel('Num of Views')
# plt.ylabel('Num of Likes')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.2f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(formatter))

plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Number of Likes', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()
In [85]:
plt.figure(figsize=(14,8))
ax = sns.regplot(data = data_clean, x='views', y='likes', scatter_kws={'alpha':0.5})
# plt.figure(figsize=(14,8))
# plt.plot(data_clean['views'], data_clean['likes'], 'o',markersize=10, alpha=0.2)
plt.axis([0,0.2e8,0,0.6e6])
# plt.xlabel('Num of Views')
# plt.ylabel('Num of Likes')
formatter = ticker.FuncFormatter(func=lambda x,y: '{:.2f}M'.format(x/1e6))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(formatter))
ax.yaxis.set_major_formatter(ticker.FuncFormatter(formatter))

plt.xlabel('Number of Views', fontsize=20)
plt.ylabel('Number of Likes', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Views Distribution', fontsize=20)
plt.show()
  • based on the heatmap, all correlations from numerical data are positive, Likes and views have the largest correlation value of 0.84.

Datetime Data

In [86]:
# Drop duplicated data and keep first occurence
data_first = data.drop_duplicates(subset ="video_id", keep='first')
data_first['upload_to_trending'] = (data_first['trending_date'].dt.date - data_first['publish_time'].dt.date).dt.days
data_first
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  This is separate from the ipykernel package so we can avoid doing imports until
Out[86]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count comments_disabled ratings_disabled video_error_or_removed description id category upload_to_trending
0 2kyS6SvSYSE 2017-11-14 WE WANT TO TALK ABOUT OUR MARRIAGE CaseyNeistat 22 2017-11-13 17:13:01+00:00 SHANtell martin 748374 57527 2966 15954 False False False SHANTELL'S CHANNEL - https://www.youtube.com/s... 22 People & Blogs 1
1 0mlNzVSJrT0 2017-11-14 Me-O Cats Commercial Nobrand 22 2017-04-21 06:47:32+00:00 cute|"cats"|"thai"|"eggs" 98966 2486 184 532 False False False Kittens come out of the eggs in a Thai commerc... 22 People & Blogs 207
2 STI2fI7sKMo 2017-11-14 AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -... Shawn Johnson East 22 2017-11-11 15:00:03+00:00 shawn johnson|"andrew east"|"shawn east"|"shaw... 321053 4451 1772 895 False False False Subscribe for weekly videos ▶ http://bit.ly/sj... 22 People & Blogs 3
3 KODzih-pYlU 2017-11-14 BLIND(folded) CAKE DECORATING CONTEST (with Mo... Grace Helbig 22 2017-11-11 18:08:04+00:00 itsgrace|"funny"|"comedy"|"vlog"|"grace"|"helb... 197062 7250 217 456 False False False Molly is an god damn amazing human and she cha... 22 People & Blogs 3
4 8mhTWqWlQzU 2017-11-14 Wearing Online Dollar Store Makeup For A Week Safiya Nygaard 22 2017-11-11 01:19:33+00:00 wearing online dollar store makeup for a week|... 2744430 115426 1110 6541 False False False I found this online dollar store called ShopMi... 22 People & Blogs 3
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
40883 pwGbwYAfSmg 2018-05-27 5 books worth reading this summer Bill Gates 29 2018-05-21 13:30:33+00:00 Bill|"Gates" 250175 8455 126 552 False False False If you’re looking for something to read over t... 29 Nonprofits & Activism 6
40892 lM0yu7c6lQk 2017-12-23 You're not crazy. Apple is slowing down older ... CNET 43 2017-12-22 01:16:16+00:00 Apple|"iPhone Slowdown"|"Apple Battery"|"Cultu... 110112 3547 180 1426 False False False Apple comes clean after overwhelming evidence ... 43 Shows 1
40903 Q1CFfU2gXHw 2018-01-26 Apple HomePod: Everything to know before you b... CNET 43 2018-01-24 20:08:16+00:00 Apple|"Homepod"|"smart home"|"Google Home"|"Am... 36609 810 66 220 False False False We'll break down all the new Apple HomePod det... 43 Shows 2
40908 7_FJUSBFbJM 2018-04-14 Game of Zones - Game of Zones - S5:E1: 'A Gold... Bleacher Report 43 2018-04-12 21:00:12+00:00 bleacher report|"br"|"nba"|"game of zones"|"go... 657183 24107 310 1825 False False False In the season 5 premiere of Game of Zones, the... 43 Shows 2
40924 V6ElE2xs48c 2018-05-12 Game of Zones - S5:E5: The Isle of Van Gundy Bleacher Report 43 2018-05-10 21:01:22+00:00 bleacher report|"br"|"nba"|"Stan Van Gundy"|"J... 765531 18681 461 1444 False False False On an island full of Van Gundys, Stan gets tal... 43 Shows 2

6351 rows × 18 columns

In [87]:
plt.figure(figsize=(10,8))
data_first['upload_to_trending'].plot(kind='hist', bins=np.arange(0, 4500, 250), logy=True)
plt.xlabel('Number of days between trending and upload', fontsize=20)
plt.ylabel('Freq', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Distribution of Number of days between trending and upload', fontsize=20)
plt.show()
In [88]:
data_first['upload_to_trending'].describe().to_frame()
Out[88]:
upload_to_trending
count 6351.000000
mean 22.522280
std 209.963296
min 0.000000
25% 1.000000
50% 2.000000
75% 3.000000
max 4215.000000
In [89]:
data_first[data_first['upload_to_trending'] < 3].shape[0] / data_first.shape[0] * 100
Out[89]:
73.51598173515981
In [90]:
data_first[data_first['upload_to_trending'] == 0].shape[0] / data_first.shape[0] * 100
Out[90]:
1.8894662257912138
In [91]:
data_first[data_first['upload_to_trending'] > 3000].shape[0] / data_first.shape[0] * 100
Out[91]:
0.1259644150527476
In [92]:
data_first.sort_values('upload_to_trending', ascending=False).head()
Out[92]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count comments_disabled ratings_disabled video_error_or_removed description id category upload_to_trending
7054 MJO3FmmFuh4 2018-02-05 Budweiser - Original Whazzup? ad dannotv 24 2006-07-23 08:24:11+00:00 Budweiser|"Bud"|"Whazzup"|"ad" 258506 459 152 82 False False False Original Whazzup ad - however, there is a litt... 24 Entertainment 4215
19639 UQtt9I6c-YM 2018-01-06 Kramer vs Kramer-Clou Scene Livia Giustiniani 1 2008-04-05 18:22:40+00:00 Meryl|"Streep"|"kramer"|"vs"|"dustin"|"hoffman... 49942 46 6 26 False False False Poor Meryl...she was really scaredxD 1 Film & Animation 3563
26483 wFEAoF7RC7Y 2017-11-25 Behind The Sounds: That's Not Me BehindTheSounds 10 2008-06-17 00:07:56+00:00 Beach|"Boys"|"Brian"|"Wilson"|"Dennis"|"Carl"|... 86744 469 4 105 False False False A video detailing the creative process behind ... 10 Music 3448
26522 Y8JGfi4MJ8k 2017-11-26 SAOIRSE RONAN - MORONIC (IRONIC) flirppy242 10 2008-08-07 12:16:58+00:00 SAOIRSE|"RONAN"|"MORONIC"|"IRONIC"|"IZZIE"|"CO... 26482 78 1 7 False False False EL NOVIO DE MI MADRE\r\nI COULD NEVER BE A WOMAN 10 Music 3398
19087 rO_mAQC9bv0 2017-11-19 Amazon.com commercial Xmas 1999Magnetic Whale Art astroboy1960 1 2009-03-10 00:27:20+00:00 amazon|"commerical"|"y2k"|"sing"|"along"|"mitc... 15382 33 1 10 False False False IMHO the best of Amazon's Christmas 1999 ads. ... 1 Film & Animation 3176
  • 73.51% of videos become trending in less than 3 days

  • only 1.88% of videos are trending on the same day as when they were uploaded

  • largest Number of days between trending and upload is 4125 days or about 11 years, the video is titled 'Budweiser - Original Whazzup? ad' uploaded by dannotv

In [93]:
trending_days = data['video_id'].value_counts().to_frame().reset_index()
trending_days.rename(columns={"index":'video_id','video_id':'trending_days'}, inplace=True)
In [94]:
data_merge = pd.merge(data_first, trending_days, on='video_id')
In [95]:
data_merge['trending_days'].describe().to_frame()
Out[95]:
trending_days
count 6351.000000
mean 6.447646
std 4.642806
min 1.000000
25% 3.000000
50% 6.000000
75% 8.000000
max 30.000000
In [96]:
plt.figure(figsize=(8,6))
data_merge['trending_days'].plot(kind='hist')
plt.xlabel('Total of Trending Days', fontsize=20)
plt.ylabel('Freq', fontsize=20)
plt.yticks(fontsize=20)
plt.xticks(fontsize=20)
plt.title('Distribution of Total Trending Days', fontsize=20)
plt.show()
In [97]:
data_sorted = data[['video_id', 'trending_date']].sort_values(['video_id', 'trending_date'])
s = data_sorted.groupby('video_id').trending_date.diff().dt.days.ne(1).cumsum()
s
data_sorted = data_sorted.groupby(['video_id', s]).size().reset_index(level=1, drop=True)
In [98]:
data_sorted = data_sorted.reset_index()
data_sorted.rename(columns={0:'consecutive_trending_days'}, inplace=True)
data_sorted
Out[98]:
video_id consecutive_trending_days
0 -0CMnp02rNY 6
1 -0NYY8cqdiQ 1
2 -1Hm41N0dUs 3
3 -1yT-K3c6YI 4
4 -2RVw2_QyxQ 3
... ... ...
6954 zxUwbflE1SY 5
6955 zxwfDlhJIpw 13
6956 zxwfDlhJIpw 10
6957 zy0b9e40tK8 1
6958 zzQsGL_F9_c 2

6959 rows × 2 columns

In [99]:
data_sorted.sort_values('consecutive_trending_days', ascending=False)
Out[99]:
video_id consecutive_trending_days
6257 t4pRQ0jn23Q 28
811 6S9c5nnDd_s 28
2978 QBL8IRJ5yHU 28
2526 MAjY8mCTXWk 28
5987 r-3iathMo7o 28
... ... ...
6030 rL4WkebTT_U 1
6029 rJfFxDQ-8ok 1
3065 R9s5_Ji33xY 1
3070 RBpLVXNN6i0 1
3479 V1Pl8CzNzCw 1

6959 rows × 2 columns

In [100]:
data_cons = pd.merge(data_first, data_sorted, on='video_id')
In [101]:
data_cons['consecutive_trending_days'].describe().to_frame()
Out[101]:
consecutive_trending_days
count 6959.000000
mean 5.884322
std 4.367000
min 1.000000
25% 3.000000
50% 5.000000
75% 8.000000
max 28.000000
In [102]:
plt.figure(figsize=(8,6))
data_cons['consecutive_trending_days'].plot(kind='hist')
plt.xlabel('Consecutive Trending Days', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Distribution of Consecutive Trending Days', fontsize=15)
plt.show()
In [103]:
data_cons.sort_values('consecutive_trending_days', ascending=False).head(10)
Out[103]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count comments_disabled ratings_disabled video_error_or_removed description id category upload_to_trending consecutive_trending_days
6642 QBL8IRJ5yHU 2018-05-15 Why I'm So Scared (being myself and crying too... grav3yardgirl 26 2018-05-14 19:00:01+00:00 beauty|"how to"|"makeup"|"howto"|"style"|"fash... 1469627 188652 3124 33032 False False False I will never be able to say Thank You enough..... 26 Howto & Style 1 28
3332 r-3iathMo7o 2018-05-15 The ULTIMATE $30,000 Gaming PC Setup Unbox Therapy 28 2018-05-13 19:00:25+00:00 gaming setup|"gaming"|"setup"|"fortnite"|"fort... 4700460 103430 8028 13293 False False False Fortnite, PUBG, Far Cry 5? Which game would yo... 28 Science & Technology 2 28
2183 t4pRQ0jn23Q 2018-05-15 YoungBoy Never Broke Again Goes Sneaker Shoppi... Complex 24 2018-05-14 14:00:03+00:00 sneakerhead|"complex"|"complex originals"|"sne... 1199587 49709 2380 7261 False False False YoungBoy Never Broke Again goes Sneaker Shoppi... 24 Entertainment 1 28
2175 j4KvrAUjn6c 2018-05-14 WE MADE OUR MOM CRY...HER DREAM CAME TRUE! Lucas and Marcus 24 2018-05-13 18:03:56+00:00 Lucas and Marcus|"Marcus and Lucas"|"Dobre"|"D... 2131702 54280 4696 10705 False False False BEST MOM EVER! TIX ON SALE NOW!! https://www.d... 24 Entertainment 1 28
3670 6S9c5nnDd_s 2018-05-16 Bohemian Rhapsody | Teaser Trailer [HD] | 20th... 20th Century Fox 1 2018-05-15 13:01:12+00:00 Trailer|"bohemian rhapsody"|"queen"|"freddie m... 4215671 161058 2198 10289 False False False Visit http://www.PutMeInBohemian.com for a cha... 1 Film & Animation 1 28
3669 NBSAQenU2Bk 2018-05-15 Rooster Teeth Animated Adventures - Millie So ... Rooster Teeth 1 2018-05-14 13:00:01+00:00 Rooster Teeth|"RT"|"animation"|"television"|"f... 404162 17920 79 642 False False False Millie is invited to help out at a Sugar Pine ... 1 Film & Animation 1 28
5556 MAjY8mCTXWk 2018-05-15 周杰倫 Jay Chou【不愛我就拉倒 If You Don't Love Me, It's... 杰威爾音樂 JVR Music 10 2018-05-14 15:59:47+00:00 周杰倫|"Jay"|"Chou"|"周董"|"周杰伦"|"周傑倫"|"杰威尔"|"周周"|"... 916128 40485 1042 4746 False False False 詞:周杰倫、宋健彰(彈頭) 曲:周杰倫憂鬱型男的走心旋律 用英式搖滾宣洩情傷不愛我就拉倒... 10 Music 1 28
5565 H7gh2fmdjCU 2018-05-18 Calum Scott - What I Miss Most CalumScottVEVO 10 2018-05-16 23:00:00+00:00 Calum|"Scott"|"What"|"Miss"|"Most"|"Capitol"|"... 664458 45750 289 2354 False False False Calum’s debut album ‘Only Human’ feat. “You Ar... 10 Music 2 27
2894 1purAy2MsOc 2018-05-16 22 Years of Life Milestone TheOdd2sOut 23 2018-05-14 19:36:05+00:00 Birthday|"rice"|"cake"|"one million"|"Milestone" 688645 73735 466 23002 False False False Thanks for an amazing year!(Im pretty behind o... 23 Comedy 2 27
5564 6SuMbFuKDf8 2018-05-18 Backstreet Boys - Don't Go Breaking My Heart (... BackstreetBoysVEVO 10 2018-05-17 04:00:01+00:00 Backstreet Boys|"Don't Go Breaking My Heart"|"... 2822391 131909 5164 16487 False False False Get the Backstreet Boys new single “Don’t Go B... 10 Music 1 27

*

In [104]:
data['trending_month'] = data.trending_date.dt.month
In [105]:
plt.figure(figsize=(8,6))
sns.countplot(data = data, x = 'trending_month')
plt.xlabel('Month', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Number of videos by Months', fontsize=15)
plt.show()
In [106]:
print(data['trending_date'].min())
print(data['trending_date'].max())
2017-11-14 00:00:00
2018-06-14 00:00:00
In [107]:
data['publish_month'] = data.publish_time.dt.month
In [108]:
plt.figure(figsize=(8,6))
sns.countplot(data = data, x = 'publish_month')
plt.xlabel('Month', fontsize=15)
plt.ylabel('Freq', fontsize=15)
plt.yticks(fontsize=15)
plt.xticks(fontsize=15)
plt.title('Number of videos by Months', fontsize=15)
plt.show()

Segmentation

Topic Modeling on Title

In [109]:
nltk.download('averaged_perceptron_tagger')
df_title['pos_tags'] = df_title['title_tokenized'].apply(nltk.tag.pos_tag)

nltk.download('wordnet')
nltk.download('omw-1.4')
def get_wordnet_pos(tag):
    if tag.startswith('J'):
        return wordnet.ADJ
    elif tag.startswith('V'):
        return wordnet.VERB
    elif tag.startswith('N'):
        return wordnet.NOUN
    elif tag.startswith('R'):
        return wordnet.ADV
    else:
        return wordnet.NOUN
df_title['wordnet_pos'] = df_title['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])

wnl = WordNetLemmatizer()
df_title['lemmatized'] = df_title['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])

df_title['lemma_str'] = [' '.join(map(str,l)) for l in df_title['lemmatized']]
df_title.head()
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /root/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!
[nltk_data] Downloading package wordnet to /root/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to /root/nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
Out[109]:
title_cleaned title_tokenized pos_tags wordnet_pos lemmatized lemma_str
0 want talk marriage [want, talk, marriage] [(want, JJ), (talk, NN), (marriage, NN)] [(want, a), (talk, n), (marriage, n)] [want, talk, marriage] want talk marriage
1 meo cats commercial [meo, cats, commercial] [(meo, NN), (cats, NNS), (commercial, JJ)] [(meo, n), (cats, n), (commercial, a)] [meo, cat, commercial] meo cat commercial
2 affairs ex boyfriends 18million net worth goog... [affairs, ex, boyfriends, 18million, net, wort... [(affairs, NNS), (ex, VBD), (boyfriends, NNS),... [(affairs, n), (ex, v), (boyfriends, n), (18mi... [affair, ex, boyfriend, 18million, net, worth,... affair ex boyfriend 18million net worth google...
3 blindfolded cake decorating contest molly burk... [blindfolded, cake, decorating, contest, molly... [(blindfolded, VBN), (cake, NN), (decorating, ... [(blindfolded, v), (cake, n), (decorating, v),... [blindfold, cake, decorate, contest, molly, bu... blindfold cake decorate contest molly burke gr...
4 wearing online dollar store makeup week [wearing, online, dollar, store, makeup, week] [(wearing, VBG), (online, JJ), (dollar, NN), (... [(wearing, v), (online, a), (dollar, n), (stor... [wear, online, dollar, store, makeup, week] wear online dollar store makeup week
In [110]:
tf_vectorizer = CountVectorizer(max_df=0.9, min_df=25, max_features=5000)
tf = tf_vectorizer.fit_transform(df_title['lemma_str'].values.astype('U'))
tf_feature_names = tf_vectorizer.get_feature_names()
doc_term_matrix = pd.DataFrame(tf.toarray(), columns=list(tf_feature_names))
doc_term_matrix
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
Out[110]:
10 2017 2018 ad adam american announcement answer apple audio ... weird white wild win wire woman work world year youtube
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
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4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6346 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6347 0 0 0 0 0 0 0 0 2 0 ... 0 0 0 0 0 0 0 0 0 0
6348 0 0 0 0 0 0 0 0 2 0 ... 0 0 0 0 0 0 0 0 0 0
6349 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6350 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

6351 rows × 225 columns

In [111]:
lda_model = LatentDirichletAllocation(n_components=30, learning_method='online', max_iter=20, random_state=0).fit(tf)
no_top_words = 10
def display_topics(model, feature_names, no_top_words):
    for topic_idx, topic in enumerate(model.components_):
        print("Topic %d:" % (topic_idx), end=' ')
        print(" ".join([feature_names[i]
                          for i in topic.argsort()[:-no_top_words - 1:-1]]))
              
display_topics(lda_model, tf_feature_names, no_top_words)
Topic 0: new movie 10 year bad team old star man find
Topic 1: go christmas every day first new time year ever never
Topic 2: game black play man come say woman golden panther hair
Topic 3: world test big perfect minute bbc film fall first christmas
Topic 4: video official ft try lyric music part explain tutorial family
Topic 5: star war top house nfl tour episode apple miss avenger
Topic 6: make best diy food battle interview work kevin cut stop
Topic 7: react use song mv iphone google hot make bts live
Topic 8: 2017 full know youtube great tb face like react use
Topic 9: get makeup week name friend car ad bts star perfect
Topic 10: live news watch stephen smith call president moment harry bowl
Topic 11: look night inside red dead high film lip first bbc
Topic 12: like scene secret people behind hit final show get refinery29
Topic 13: take last kid jedi school dance camila secret show star
Topic 14: love cake baby hot kim announcement grace name vs know
Topic 15: voice fire date justin lose cast wire kim national iphone
Topic 16: first time super bowl meet nba everything story girl love
Topic 17: one life thing buy march fox ad second vs bbc
Topic 18: ever chris national find never get hot christmas grace weird
Topic 19: vs real girl question vogue bts tv wire tom answer
Topic 20: show dog open snl white back hart youtube nbc fall
Topic 21: award american music win nbc fall let performance 2017 kim
Topic 22: trailer official hd talk season netflix teaser fan honest cook
Topic 23: espn john break giant shoot theory eat go netflix kevin
Topic 24: challenge highlight review box league grace adam audition food makeup
Topic 25: audio beauty feat official see cardi cover teaser make live
Topic 26: cat commercial car sport ice mom lip bowl challenge get
Topic 27: james face refinery29 holiday michael lebron olympics fall hot fire
Topic 28: 2018 trump wild give special jimmy need want nbc trailer
Topic 29: day home ball car minute use movie dog special let
In [ ]:
pyLDAvis.enable_notebook()
panel = pyLDAvis.sklearn.prepare(lda_model, tf, tf_vectorizer, mds='tsne')
panel
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  by='saliency', ascending=False).head(R).drop('saliency', 1)
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:827: FutureWarning: 'square_distances' has been introduced in 0.24 to help phase out legacy squaring behavior. The 'legacy' setting will be removed in 1.1 (renaming of 0.26), and the default setting will be changed to True. In 1.3, 'square_distances' will be removed altogether, and distances will be squared by default. Set 'square_distances'=True to silence this warning.
  FutureWarning,
In [ ]:
lda_topic_values = lda_model.transform(tf)
data_first['lda_title'] = lda_topic_values.argmax(axis=1)
In [114]:
cnt_lda_title = data_first['lda_title'].value_counts().to_frame().reset_index()
cnt_lda_title.rename(columns = {'index':'Topic', 'lda_title':'Freq'}, inplace=True)
cnt_lda_title.Topic = cnt_lda_title.Topic.astype('str')
plt.figure(figsize=(20,10))
sns.barplot(data = cnt_lda_title.sort_values('Freq', ascending = False), x = 'Freq', y = 'Topic')
plt.title("LDA Topic Distribution on Title", fontsize=20)
plt.ylabel('Review Topics', fontsize=20)
plt.yticks(fontsize=20)
plt.xlabel('Frequency', fontsize=20)
plt.xticks(fontsize=20)

Topic Modeling on Tags

In [115]:
df_tags['pos_tags'] = df_tags['tags_tokenized'].apply(nltk.tag.pos_tag)

df_tags['wordnet_pos'] = df_tags['pos_tags'].apply(lambda x: [(word, get_wordnet_pos(pos_tag)) for (word, pos_tag) in x])

wnl = WordNetLemmatizer()
df_tags['lemmatized'] = df_tags['wordnet_pos'].apply(lambda x: [wnl.lemmatize(word, tag) for word, tag in x])

df_tags['lemma_str'] = [' '.join(map(str,l)) for l in df_tags['lemmatized']]
df_tags.head()
Out[115]:
tags_cleaned tags_tokenized pos_tags wordnet_pos lemmatized lemma_str
0 shantell martin [shantell, martin] [(shantell, NN), (martin, NN)] [(shantell, n), (martin, n)] [shantell, martin] shantell martin
1 cute cats thai eggs [cute, cats, thai, eggs] [(cute, NN), (cats, NNS), (thai, VBP), (eggs, ... [(cute, n), (cats, n), (thai, v), (eggs, n)] [cute, cat, thai, egg] cute cat thai egg
2 shawn johnson andrew east shawn east shawn and... [shawn, johnson, andrew, east, shawn, east, sh... [(shawn, NN), (johnson, NN), (andrew, VBD), (e... [(shawn, n), (johnson, n), (andrew, v), (east,... [shawn, johnson, andrew, east, shawn, east, sh... shawn johnson andrew east shawn east shawn and...
3 itsgrace funny comedy vlog grace helbig graceh... [itsgrace, funny, comedy, vlog, grace, helbig,... [(itsgrace, NN), (funny, JJ), (comedy, NN), (v... [(itsgrace, n), (funny, a), (comedy, n), (vlog... [itsgrace, funny, comedy, vlog, grace, helbig,... itsgrace funny comedy vlog grace helbig graceh...
4 wearing online dollar store makeup week online... [wearing, online, dollar, store, makeup, week,... [(wearing, VBG), (online, JJ), (dollar, NN), (... [(wearing, v), (online, a), (dollar, n), (stor... [wear, online, dollar, store, makeup, week, on... wear online dollar store makeup week online do...
In [116]:
tf_vectorizer = CountVectorizer(max_df=0.9, min_df=25, max_features=5000)
tf = tf_vectorizer.fit_transform(df_tags['lemma_str'].values.astype('U'))
tf_feature_names = tf_vectorizer.get_feature_names()
doc_term_matrix = pd.DataFrame(tf.toarray(), columns=list(tf_feature_names))
doc_term_matrix
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
Out[116]:
08282016ntflxuscan 10 100 12 13 15 16 2017 2018 29 ... youtube youtuber youtubers مصارعه कशत डबल पहलवन मच वयवसयक सपरसटर
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 ... 0 2 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
6346 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6347 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6348 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6349 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6350 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

6351 rows × 1012 columns

In [117]:
lda_model = LatentDirichletAllocation(n_components=30, learning_method='online', max_iter=20, random_state=0).fit(tf)
no_top_words = 10
              
display_topics(lda_model, tf_feature_names, no_top_words)
Topic 0: school high hack ever standup honest learn blue tesla amaze
Topic 1: animal take big king jack documentary great wild fry jr
Topic 2: espn perfect go charlie health bbc nick ted heart way
Topic 3: challenge react kid google liza reaction nfl brother fine know
Topic 4: make diy disney fox year box nintendo homemade world hannah
Topic 5: news smith nba trump basketball game animation live lebron break
Topic 6: interview sport celebrity none football entertainment game tonight magazine soccer
Topic 7: tv television car netflix series original john watch movie online
Topic 8: test voice buzzfeed food team try people taste best eat
Topic 9: 2017 live christmas award red 2018 holiday seth best meyers
Topic 10: makeup tutorial beauty black get hair face pet random video
Topic 11: family girl noggin funny anthony friendly fun boy robot lol
Topic 12: food recipe cook cake water kitchen hot bake world chef
Topic 13: star war last jedi bts cardi mark scene johnson explain
Topic 14: super bowl commercial chocolate green ice ad pizza money power
Topic 15: ellen dog man marvel talent show date fan degeneres panther
Topic 16: movie trailer 2018 film official theory new royal american ryan
Topic 17: life vlog house day home tour alex real fire prank
Topic 18: video cat show nbc today funny amazon kitten cute race
Topic 19: first time olympics woman ball winter 2018 meet evans korea
Topic 20: youtube channel simon education grace steve party audition press reveal
Topic 21: new science one york vs space city video adam travel
Topic 22: fashion style room trick trend secret every work fall like
Topic 23: night live snl sketch chris comedy season episode taylor host
Topic 24: game iphone review apple top 10 art design tech best
Topic 25: baby kardashian kim link good rhett news morning parody mythical
Topic 26: show late funny comedy talk stephen video joke humor celebrity
Topic 27: music video song pop record official play new love lyric
Topic 28: highlight fallon james graham lip full kelly battle bad sing
Topic 29: dance thing wwe kevin street la shot wrestle camera lilly
In [118]:
pyLDAvis.enable_notebook()
panel = pyLDAvis.sklearn.prepare(lda_model, tf, tf_vectorizer, mds='tsne')
panel
/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.
  warnings.warn(msg, category=FutureWarning)
/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  by='saliency', ascending=False).head(R).drop('saliency', 1)
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.
  FutureWarning,
/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:827: FutureWarning: 'square_distances' has been introduced in 0.24 to help phase out legacy squaring behavior. The 'legacy' setting will be removed in 1.1 (renaming of 0.26), and the default setting will be changed to True. In 1.3, 'square_distances' will be removed altogether, and distances will be squared by default. Set 'square_distances'=True to silence this warning.
  FutureWarning,
Out[118]:
In [119]:
lda_topic_values = lda_model.transform(tf)
data_first['lda_tags'] = lda_topic_values.argmax(axis=1)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  
In [120]:
cnt_lda_tags = data_first['lda_tags'].value_counts().to_frame().reset_index()
cnt_lda_tags.rename(columns = {'index':'Topic', 'lda_tags':'Freq'}, inplace=True)
cnt_lda_tags.Topic = cnt_lda_tags.Topic.astype('str')
plt.figure(figsize=(20,10))
sns.barplot(data = cnt_lda_tags.sort_values('Freq', ascending = False), x = 'Freq', y = 'Topic')
plt.title("LDA Topic Distribution on Tags", fontsize=20)
plt.ylabel('Review Topics', fontsize=20)
plt.yticks(fontsize=20)
plt.xlabel('Frequency', fontsize=20)
plt.xticks(fontsize=20)
Out[120]:
(array([  0., 100., 200., 300., 400., 500., 600.]),
 <a list of 7 Text major ticklabel objects>)
In [121]:
data_first.head()
Out[121]:
video_id trending_date title channel_title category_id publish_time tags views likes dislikes comment_count comments_disabled ratings_disabled video_error_or_removed description id category upload_to_trending lda_title lda_tags
0 2kyS6SvSYSE 2017-11-14 WE WANT TO TALK ABOUT OUR MARRIAGE CaseyNeistat 22 2017-11-13 17:13:01+00:00 SHANtell martin 748374 57527 2966 15954 False False False SHANTELL'S CHANNEL - https://www.youtube.com/s... 22 People & Blogs 1 22 20
1 0mlNzVSJrT0 2017-11-14 Me-O Cats Commercial Nobrand 22 2017-04-21 06:47:32+00:00 cute|"cats"|"thai"|"eggs" 98966 2486 184 532 False False False Kittens come out of the eggs in a Thai commerc... 22 People & Blogs 207 26 18
2 STI2fI7sKMo 2017-11-14 AFFAIRS, EX BOYFRIENDS, $18MILLION NET WORTH -... Shawn Johnson East 22 2017-11-11 15:00:03+00:00 shawn johnson|"andrew east"|"shawn east"|"shaw... 321053 4451 1772 895 False False False Subscribe for weekly videos ▶ http://bit.ly/sj... 22 People & Blogs 3 7 3
3 KODzih-pYlU 2017-11-14 BLIND(folded) CAKE DECORATING CONTEST (with Mo... Grace Helbig 22 2017-11-11 18:08:04+00:00 itsgrace|"funny"|"comedy"|"vlog"|"grace"|"helb... 197062 7250 217 456 False False False Molly is an god damn amazing human and she cha... 22 People & Blogs 3 14 17
4 8mhTWqWlQzU 2017-11-14 Wearing Online Dollar Store Makeup For A Week Safiya Nygaard 22 2017-11-11 01:19:33+00:00 wearing online dollar store makeup for a week|... 2744430 115426 1110 6541 False False False I found this online dollar store called ShopMi... 22 People & Blogs 3 9 10
In [122]:
# %%shell
# jupyter nbconvert --to html //Your notebook path file.ipynb